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Data Mining  Association Rule - Basic Concepts
 
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short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Database VS Data Warehouse
 
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Whats the difference between a Database and a Data Warehouse? I had a attendee ask this question at one of our workshops. In this short video I explain the distinction. Here's the link mentioned in the video:https://intricity.attach.io/r1x~TiWdz Talk with an Intricity Specialist: https://www.intricity.com/intricity101/
Views: 9534 Intricity101
Introduction to Datawarehouse in hindi | Data warehouse and data mining Lectures
 
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#datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 296300 Last moment tuitions
MINING HIGH UTILITY ITEM SETS IN TRANSACTIONAL DATABASE
 
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Data mining is the process of revealing nontrivial,previously unknown and potentially useful information from large databases. Discovering useful patterns hidden in the database plays an essential role in several data mining tasks,such as frequent pattern mining, weighted pattern mining and high utility pattern mining. This Project aims at mining the different combination of itemsets with high utility like profits from the transactional database. Utility based data mining is a new research area interested in all types of utility factors in data mining processes and targeted at incorporating utility considerations in data mining tasks. The UMining algorithm is used to find all high utility itemsets within the given utility constraint threshold. This algorithm has a pruning strategy of its own. Fast Utility Mining is a novel algorithm which is faster and simpler than the original UMining algorithm for generating high utility itemsets. The experimental evaluation on artificial datasets show that this algorithm executes faster than UMining algorithm. Another algorithm, Fast Utility Frequent Mining, is a more precise and very recent algorithm. It takes both the utility and the support measure into consideration.
Views: 781 Deepika Starz
Final Year Projects | Privacy-Preserving Mining of Association Rules From Outsourced Transaction
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 841 myproject bazaar
Differentially Private Frequent Itemset Mining via Transaction Splitting | Final Year Projects 2016
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 47 Clickmyproject
Privacy Preserving Mining Of Association Rules From Outsourced Transaction Databases
 
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ChennaiSunday Systems Pvt.Ltd We are ready to provide guidance to successfully complete your projects and also download the abstract, base paper from our website IEEE 2013 Java: http://www.chennaisunday.com/ieee-2013-java-projects.html Out Put: http://www.youtube.com/channel/UCpo4sL0gR8MFTOwGBCDqeFQ IEEE 2013 Dot Net: http://www.chennaisunday.com/ieee-2013-Dotnet-projects.html IEEE 2012 Java: http://www.chennaisunday.com/ieee-2012-java-projects.html Out Put: http://www.youtube.com/channel/UC87_vSNJbLNmevUSseNE_vw IEEE 2012 Dot Net: http://www.chennaisunday.com/ieee-2012-projects.html IEEE 2011 JAVA: http://www.chennaisunday.com/ieee-2011-java-projects.html Out Put: http://www.youtube.com/channel/ UCLI3FPJiDQR6s6Y3BPsPqQ IEEE 2011 DOT NET: http://www.chennaisunday.com/ieee-2011-projects.html Out Put: http://www.youtube.com/channel/UC4nV8PIFppB4r2wF5N4ipqA/videos IEEE 2010 JAVA: http://www.chennaisunday.com/ieee-2010-java-projects.html IEEE 2010 DOT NET: http://www.chennaisunday.com/ieee-2010-dotnet-projects.html Real Time APPLICATION: http://www.chennaisunday.com/softwareprojects.html Contact: 9566137117/ 044-42046569 Model Video: http://www.youtube.com/channel/UCpo4sL0gR8MFTOwGBCDqeFQ/videos -- *Contact * * P.Sivakumar MCA Director ChennaiSunday Systems Pvt Ltd Phone No: 09566137117 New No.82, 3rd Floor, Arcot Road, Kodambakkam, Chennai - 600 024. URL: www.chennaisunday.com Location: http://www.chennaisunday.com/mapview.html
Views: 887 Shiva Kumar
Data Mining Primer
 
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Data Mining Primer - A powerpoint presentation I made. Please read VLDB as Very Large Databases Relational or NonRelational. Some databases may be transactional in which case they will follow ACID rules and knowledge about transaction isolation levels may be helpful in those transactional big data mining situations.
Views: 17 L.Mohan Arun
Data Mining Lecture - - Finding frequent item sets | Apriori Algorithm | Solved Example (Eng-Hindi)
 
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In this video Apriori algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining in hindi, Finding frequent item sets, data mining, data mining algorithms in hindi, data mining lecture, data mining tools, data mining tutorial,
Views: 228401 Well Academy
Data Warehouse tutorial. Creating an ETL.
 
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This Data Warehouse video tutorial demonstrates how to create ETL (Extract, Load, Transform) package. See more lessons http://www.learn-with-video-tutorials.com/data-warehouse-tutorial-video
Study of Database Intrusion Detection Based on Improved Association Rule Algorithm
 
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itle: Study of Database Intrusion Detection Based on Improved Association Rule Algorithm Domain: Data Mining Description: The proposed work is a hybrid approach that contains the detection of malicious and intrusive activity by combining two techniques, one is of association rule and second is Log mining. By combining these two methods we can achieve better efficiency by finding accurate intrusion in the database. The proposed method can be place on database management level and thus provide security to the database. The existing systems have limitations of missing few intrusions and high false positive rates and also they have overhead of creating profiles and keeping record of all the activities and update the large database every time. Intrusion detection technology refers to identify any activities of damage to the computer system security, integrity and confidentiality Different from the traditional operating system reinforcement, authentication and firewall security isolation technology, intrusion detection as an active dynamic security defence technologies, it provides internal attacks and external attacks and misuse in real-time protection. Data mining is an interdisciplinary field, affected by a number of disciplines, including database systems, statistics, machine learning, visualization and information science. There are many data mining methods commonly used in database intrusion detection, in which the association rule mining algorithm and sequential pattern mining algorithm are widely applied in particular. Association rule is to find the correlation of different items appeared in the same event. Association rule mining is to derive the implication relationships between data items under the conditions of a set of given project types and a number of records and through analyzing the records, the commonly used algorithm is Apriori algorithm. Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. contact for more details: 044-43548566,8110081181 [email protected]
Views: 114 SHPINE TECHNOLOGIES
Data Mining
 
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Technology students give presentation on about Data Mining including the advantages/disadvantages, how to and more.
Views: 17124 techEIU
Introduction to Data warehouse  and difference between Database and Data warehouse
 
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Introduction to Data warehouse and difference between Database and Data warehouse more videos In computer science, ACID (Atomicity, Consistency, Isolation, Durability) is a set of properties that guarantee that database transactions are processed reliably. In the context of databases, a single logical operation on the data is called a transaction. http://atozknowledge.com/ Technology in Tamil
Views: 19787 atoz knowledge
The History Of Blockchain Explained
 
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To find out more about how blockchain works, check out our free guide: https://www.cryptomaniaks.com/learn/the-blockchain/get-started We’ll start at the very beginning by understanding the history of blockchain. By using math and cryptography, Blockchain is challenging the status quo in a radical way. Will governments and financial institutions embrace it? ●▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬● 🏠 Website: www.cryptomaniaks.com 👨‍🎓Free Courses on Bitcoin, Blockchain, Cryptocurrency Investment ... https://www.cryptomaniaks.com/learn 🤑Step-by-Step Guide to Get Started Investing in Cryptocurrency: https://cryptomaniaks.com/ultimate-beginners-guide-invest-cryptocurrency/course/start-investing 👤Facebook Page: http://bit.ly/PageManiaks ●▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬● 💰Buy Bitcoin/Ethereum On CoinBase: https://rebrand.ly/coinbase-CryptoManiaks 👻Trade Cryptos On Binance: https://rebrand.ly/binance-CryptoManiaks 🔒Secure your Cryptos with Ledger Nano S: https://rebrand.ly/ledger-CryptoManiaksManiaks ●▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬● The very first blockchain in the world was Bitcoin. An anonymous person or group known as Satoshi Nakamoto published a document in an online cryptography forum in November 2008 and revealed the first details of how it would work, describing it as a “peer-to-peer electronic cash system”. The whitepaper is available today at bitcoin.org/bitcoin.pdf. It allows any 2 people to pseudonymously send money to each other no matter where they are in the world. It is a borderless currency. The main benefit of Bitcoin is that it does not require any centralized authority or institution to operate. This is in contrast to today’s centralized financial systems that depend on the existence of a central bank or government to mint money. If for any reason the central authority were to shutdown, the money would become worthless. In a decentralized system like Bitcoin, there is no central authority and the system can continue to operate as long as there are members in its peer-to-peer network. The goal of the whitepaper was to describe how the different parts of the Bitcoin protocol would operate and be kept secure. A new type of database, called a blockchain, would keep track of a single history of all Bitcoin transactions and it would be maintained by everyone in the network. The database would be publicly available for anyone to view and inspect, and anyone can download a copy of the same database. This provides data redundancy and makes sure the data is never lost, but also provides a way for anyone to verify the transactions in the database themselves. A block in the database just stores a sequence of transactions, and a sequence of blocks is called a blockchain. Each block is identified by an incrementing number and a unique Sha-256 hash. The hash for a block is calculated using the transactions inside it, as well as the previous block’s hash, which forms a chain of hashes. The data in the blocks is secured using a cryptographic algorithm called proof-of-work, which also keeps all members of the network and the database in sync to prevent double-spending. In this context, preventing double-spending means preventing anyone from spending money they dont have. Proof-of-work is used to generate new blocks for the database, also known as mining, and the reward for mining a new block is given to the miner by creating new Bitcoins in the system. This is the only way new Bitcoins can be created. Anyone on the network can be a miner and a new block is mined roughly every 10 minutes, which includes the latest set of verified transactions. The first release for Bitcoin was version 0.1 written in C++ by Satoshi and published on SourceForge in January 2009 under the open-source MIT license. Anyone could download the source code and run it to join the network, also known as becoming a node in the network. This is the original version 0.1 source code written by Satoshi. We can see the hard-coded genesis block, which is the very first block in the chain. The hash for the block can be verified by using any Bitcoin blockchain explorer. Let’s copy and paste this hash into the blockchain explorer available at blockchain.info. We can see that this hash is for block number 0, and that it has only one transaction in it which is the mining reward, and the reward amount of 50 Bitcoin was given to this Bitcoin address. We can also see this 50 Bitcoin reward for the genesis block in the original source code. The genesis block is a special case needed to start the blockchain and is the only block that is hard-coded, whereas every subsequent block is calculated using proof-of-work. Satoshi’s motivation for creating Bitcoin is revealed in the piece of data he included in the genesis block: a newspaper ...
Views: 623 CryptoManiaks
Clustering Individual Transactional Data for Masses of Users
 
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Author: Riccardo Guidotti, National Research Council (CNR) Abstract: Mining a large number of datasets recording human activities for making sense of individual data is the key enabler of a new wave of personalized knowledge-based services. In this paper we focus on the problem of clustering individual transactional data for a large mass of users. Transactional data is a very pervasive kind of information that is collected by several services, often involving huge pools of users. We propose txmeans, a parameter-free clustering algorithm able to efficiently partitioning transactional data in a completely automatic way. Txmeans is designed for the case where clustering must be applied on a massive number of different datasets, for instance when a large set of users need to be analyzed individually and each of them has generated a long history of transactions. A deep experimentation on both real and synthetic datasets shows the practical effectiveness of txmeans for the mass clustering of different personal datasets, and suggests that txmeans outperforms existing methods in terms of quality and efficiency. Finally, we present a personal cart assistant application based on txmeans. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 416 KDD2017 video
Google I/O 2009 - Transactions Across Datacenters..
 
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Google I/O 2009 - Transactions Across Datacenters (and Other Weekend Projects) Ryan Barrett -- Contents -- 0:55 - Background quotes 2:30 - Introduction: multihoming for read/write structured storage 5:12 - Three types of consistency: weak, eventual, strong 10:00 - Transactions: definition, background 12:22 - Why multihome? Why try do anything across multiple datacenters? 15:30 - Why *not* multihome? 17:45 - Three kinds of multihoming: none, some, full 27:35 - Multihoming techniques and how to evaluate them 28:30 - Technique #1: Backups 31:39 - Technique #2: Master/slave replication 35:42 - Technique #3: Multi-master replication 39:30 - Technique #4: Two phase commit 43:53 - Technique #5: Paxos 49:35 - Conclusion: no silver bullet. Embrace the tradeoffs! 52:15 - Questions -- End -- If you work on distributed systems, you try to design your system to keep running if any single machine fails. If you're ambitious, you might extend this to entire racks, or even more inconvenient sets of machines. However, what if your entire datacenter falls off the face of the earth? This talk will examine how current large scale storage systems handle fault tolerance and consistency, with a particular focus on the App Engine datastore. We'll cover techniques such as replication, sharding, two phase commit, and consensus protocols (e.g. Paxos), then explore how they can be applied across datacenters. For presentation slides and all I/O sessions, please go to: code.google.com/events/io/sessions.html
Views: 31111 Google Developers
Frequent Pattern (FP) growth Algorithm for Association Rule Mining
 
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The FP-Growth Algorithm, proposed by Han, is an efficient and scalable method for mining the complete set of frequent patterns by pattern fragment growth, using an extended prefix-tree structure for storing compressed and crucial information about frequent patterns named frequent-pattern tree (FP-tree).
Views: 109851 StudyKorner
Business Intelligence: Multidimensional Analysis
 
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An introduction to multidimensional business intelligence and OnLine Analytical Processing (OLAP) suitable for both a technical and non-technical audience. Covers dimensions, attributes, measures, Key Performance Indicators (KPIs), aggregates, hierarchies, and data cubes. Downloadable slides available from SlideShare at http://goo.gl/4tIjVI
Views: 61821 Michael Lamont
Distributed Databases - Transparency, Replication, Horizontal and Vertical Fragmentation, Allocation
 
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Exclusive range of revision notes & video lessons available on our site |||--- ClicK LINK To ViSiT ---||| http://www.studyyaar.com/index.php/module/19-distributed-and-parallel-database This video clip is part of module available at http://www.studyyaar.com/index.php/learning-program/7-database-management-system-part-2
Views: 80918 StudyYaar.com
Relational Database Concepts
 
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Basic Concepts on how relational databases work. Explains the concepts of tables, key IDs, and relations at an introductory level. For more info on Crow's Feet Notation: http://prescottcomputerguy.com/tmp/crows-foot.png
Views: 593952 Prescott Computer Guy
Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning
 
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Apriori Algorithm (Associated Learning) - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Limited Time - Discount Coupon Apriori Algorithm The Apriori algorithm is a classical algorithm in data mining that we can use for these sorts of applications (i.e. recommender engines). So It is used for mining frequent item sets and relevant association rules. It is devised to operate on a database containing a lot of transactions, for instance, items brought by customers in a store. It is very important for effective Market Basket Analysis and it helps the customers in purchasing their items with more ease which increases the sales of the markets. It has also been used in the field of healthcare for the detection of adverse drug reactions. A key concept in Apriori algorithm is that it assumes that: 1. All subsets of a frequent item sets must be frequent 2. Similarly, for any infrequent item set, all its supersets must be infrequent too. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 63152 Augmented Startups
Databases & Data Warehouses, Data: Structures, Types, Integrations
 
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Dr. Chaitan Baru and Dr. Elena Zheleva from the National Science Foundation presents a lecture on "Databases & Data Warehouses, Data: Structures, Types, Integrations" Lecture Abstract This talk will provide an overview of the evolution of database and data warehouse approaches and technologies. Where did we begin, and where have we come to? In the process, we will provide a review of concepts like structured and semistructured databases; schema on-write versus schema on-read; SQL/noSQL database; data integration; and data integrity constraints. The talk will be motivated by example of Data Science use cases. View slides from this lecture https://drive.google.com/open?id=0B4IAKVDZz_JUck8tQlJ0N2VTSlU About the Speakers: Chaitan Baru is Senior Advisor for Data Science in the Computer and Information Science and Engineering (CISE) Directorate at the National Science Foundation, where he coordinates the cross-Foundation BIGDATA research program, advises the NSF Big Data Hubs and Spokes program, assists in CISE strategic planning in Data Science, and participates in interdisciplinary and inter-agency Data Science-related activities. He co-chairs the Big Data Inter-agency Working Group, and is co-author of the US Federal Big Data R&D Strategic Plan (http://bit.ly/1Ughpjt), released May 2016 by the Networking and Information Technology R&D (NITRD) group of the National Coordination Office, White House Office of Science and Technology Policy. Elena Zheleva is a computer scientist with a background in machine learning, social network analysis and online privacy. Elena has presented her research at top-tier conferences, and she is the co-author of the book "Privacy in Social Networks." After completing her Ph.D. in Computer Science from the University of Maryland College Park in 2011, Elena spent five years in industry as a data scientist, focusing on recommender systems and incentivized social sharing. She was also fortunate to intern at Microsoft Research, AOL and The Institute for Genomic Research. Currently, Elena is an AAAS Science and Technology Policy Fellow at the National Science Foundation where she contributes to big data and data science initiatives. Please join our weekly meetings from your computer, tablet or smartphone. Visit our website to learn how to join! http://www.bigdatau.org/data-science-seminars
Final Year Projects | Efficient Algorithms for Mining High Utility Itemsets from Transactional
 
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IEEE Projects 2012 | Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 2548 Clickmyproject
Lecture - 34 Data Mining and Knowledge Discovery
 
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Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 134792 nptelhrd
How does a blockchain work - Simply Explained
 
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What is a blockchain and how do they work? I'll explain why blockchains are so special in simple and plain English! 💰 Want to buy Bitcoin or Ethereum? Buy for $100 and get $10 free (through my affiliate link): https://www.coinbase.com/join/59284524822a3d0b19e11134 📚 Sources can be found on my website: https://www.savjee.be/videos/simply-explained/how-does-a-blockchain-work/ 🐦 Follow me on Twitter: https://twitter.com/savjee ✏️ Check out my blog: https://www.savjee.be ✉️ Subscribe to newsletter: https://goo.gl/nueDfz 👍🏻 Like my Facebook page: https://www.facebook.com/savjee
Views: 2832813 Simply Explained - Savjee
Efficient Algorithms For Mining High Utility Itemsets From Transactional Databases
 
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ChennaiSunday Systems Pvt.Ltd We are ready to provide guidance to successfully complete your projects and also download the abstract, base paper from our website IEEE 2013 Java: http://www.chennaisunday.com/ieee-2013-java-projects.html Out Put: http://www.youtube.com/channel/UCpo4sL0gR8MFTOwGBCDqeFQ IEEE 2013 Dot Net: http://www.chennaisunday.com/ieee-2013-Dotnet-projects.html IEEE 2012 Java: http://www.chennaisunday.com/ieee-2012-java-projects.html Out Put: http://www.youtube.com/channel/UC87_vSNJbLNmevUSseNE_vw IEEE 2012 Dot Net: http://www.chennaisunday.com/ieee-2012-projects.html IEEE 2011 JAVA: http://www.chennaisunday.com/ieee-2011-java-projects.html Out Put: http://www.youtube.com/channel/ UCLI3FPJiDQR6s6Y3BPsPqQ IEEE 2011 DOT NET: http://www.chennaisunday.com/ieee-2011-projects.html Out Put: http://www.youtube.com/channel/UC4nV8PIFppB4r2wF5N4ipqA/videos IEEE 2010 JAVA: http://www.chennaisunday.com/ieee-2010-java-projects.html IEEE 2010 DOT NET: http://www.chennaisunday.com/ieee-2010-dotnet-projects.html Real Time APPLICATION: http://www.chennaisunday.com/softwareprojects.html Contact: 9566137117/ 044-42046569 Model Video: http://www.youtube.com/channel/UCpo4sL0gR8MFTOwGBCDqeFQ/videos -- *Contact * * P.Sivakumar MCA Director ChennaiSunday Systems Pvt Ltd Phone No: 09566137117 New No.82, 3rd Floor, Arcot Road, Kodambakkam, Chennai - 600 024. URL: www.chennaisunday.com Location: http://www.chennaisunday.com/mapview.html
Views: 1860 Shiva Kumar
R - Association Rules - Market Basket Analysis (part 1)
 
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Association Rules for Market Basket Analysis using arules package in R. The data set can be load from within R once you have installed and loaded the arules package. Association Rules are an Unsupervised Learning technique used to discover interesting patterns in big data that is usually unstructured as well.
Views: 54590 Jalayer Academy
Final Year Projects | Credit card transaction fraud detection Markov model
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 5932 myproject bazaar
SQL Server Transaction Isolation Levels Dejan Sarka
 
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Breakout session from DevWeek 2017 Link for all dot net and sql server video tutorial playlists Link for slides, code samples and text version of the video . In this SQL Server Quickie Im talking about Read Committed Snapshot Isolation in SQL Server. You can find the scripts that were used for the demonstration here: SQL Server DBA Interview question How would you identify the isolation level used by the query when dead lock occurs Complete list of SQL Server DBA Interview Questions by Tech Brothers.
Views: 9 Hilda Prophet
Lecture - 30 Introduction to Data Warehousing and OLAP
 
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Lecture Series on Database Management System by Dr.S.Srinath, IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 212267 nptelhrd
Efficient Algorithms Mining Top-K High Utility Itemsets | Final Year Projects 2016 - 2017
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 154 Clickmyproject
Final Year Projects | Efficient Mining of Freqent Itemsets on large uncertain databases
 
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Final Year Projects | Efficient Mining of Freqent Itemsets on large uncertain databases More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 246 Clickmyproject
Final Year Projects | Comparison and evaluation of data mining techniques with algorithmic models
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 313 Clickmyproject
PyCon.DE 2018: ZODB: The Graph Database For PythonDevelopers - Christopher Lozinski
 
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You can see the current version of the slides at https://pythonlinks.info/presentations/zodbtalk.pdf I invite you to first watch the full but slightly earlier version of the talk at PythonLinks.info/zodb And then read the following summary to see what else is being added to the talk. The ZODB is a mature graph database written in Python and optimized in C. Just subclass off of class Persistent Object and Persistent Container, and your objects, graphs and applications become persistent. The market for Graph Databases has recently exploded, as evidenced by over $200 Million invested in graph database companies. Most of the graph databases are written in Java. If you are a Python developer, you will find much greater productivity using a graph database written in Python, than one written in statically bound Java. You cannot add or remove an attribute to an object at run-time in a statically typed language. Furthermore, the major Java databases constrain you to one of several persistent data types. Persistent Python, supported by the ZODB allows you to make any Python data structure persistent. Publishing JSON, YAML and Pickles are well supported. GraphQL is conceptually very close to the ZODB schema approach. Okay, the ZODB is interesting, but is it risky? The ZODB is mature, rock solid and well supported. The ZODB is quite heavily used in the Plone world. Just the government of Brazil has over 100 websites using the ZODB. That includes the President's office, parliament and many other governmet offices. Recently the ZODB has been reengineered. It now supports thousands of write transactions per second. The major applications of graph databases are fraud detection, social networks and computer networks. NLP is an interesting application area. The talk reviews the basic concepts of traversal and views on objects. It is important to understand the basics of how objects are stored on disk. Objects are pickled. There are multiple ways to store those pickles. When using File Storage, the objects in a transaction are appended to he end of the database files. When using relstorage, a record is created with the object id, the version number, and the pickle. The talk reviews how objects are distributed across multiple Python processes. With ZEO the pickles are served across the network. Connections are encrypted. The talk also discusses how to build real-time (chat and iOT) applications using the MQTT message broker with the ZODB. Performance, scalability, and number of objects, are all discussed. Comparisons are made to traditional relational databases. The ZODB Demo makes it very easy to start building your own applications on top of the ZODB. You can start by customizing the TreeLeaf, TreeBranch and TreeRoot classes and their templates. You get CRUD for free. The demo includes traditional relational CRUD, Create, Read, Update, and Delete. But it also includes the extended graph CRUD. Rename a Leaf or Branch. Cut and paste leaves or branches, copy and paste leaves or branches. View and restore historic versions are demonstrated. Of course the real reason to use a graph database is to improve the user experience. A basic concept in human factors is to limit lists to 7 items. That is why librarians use hierarchy. The Panama Papers journalists said a graph database was more intuitive. Have you ever selected your country from a list of 150 countries. Much better to use a hierarchical list. Have you ever used a Google map with thousands of pins. Much better to have one page for each city. And of course the most important reason for using a graph database is not what the software does, but how it changes how we humans think about our problems, and how we make decisions. Graph databases enable a different approach to distributing applications across the network. They encourage a different approach to managing the git development process. They enable a different set of decisions to be made. By the end of this talk, readers should have a much better appreciation for the rich but little known and under appreciated ZODB ecosystem.
Views: 83 PyConDE
Secure Mining of Association Rules in  Horizontally Distributed Databases
 
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Secure Mining of Association Rules in Horizontally Distributed Databases LeMeniz Infotech A Leading Software Concern Stepping in IEEE Projects 2014-2015. Do Your Projects With Domain Experts. To Get this Projects with Complete Document Call Us Rafee 9962588976 / 9566355386 Web : http://www.lemenizinfotech.com/tag/online-ieee-projects/ blog : http://www.lemenizinfotech.blogspot.in blog : http://www.ieeeprojectsinpondicherry.blogspot.in Download App from http://www.ieeeprojectspondicherry.weebly.com
Views: 120 LeMeniz Infotech
Lecture - 33 Case Study ORACLE and Microsoft Access
 
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Lecture Series on Database Management System by Dr. S. Srinath, IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 27374 nptelhrd
Lecture - 29 Recovery Mechanisms III
 
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Lecture Series on Database Management System by Dr.S.Srinath, IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 17971 nptelhrd
A New Methodology for Mining Frequent Itemsets on Temporal Data
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 104 Clickmyproject
Differentially Private Frequent Itemset Mining via Transaction Splitting | Final Year Projects 2016
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 62 myproject bazaar
The Thinking Persons Guide to Data Warehouse Design
 
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Robin Schumacher (Calpont) presents "The Thinking Person's Guide to Data Warehouse Design" at the 2010 MySQL User Conference and expo. Slides: http://www.slideshare.net/calpont/the-thinking-persons-guide-to-data-warehouse-design From the official description at: http://en.oreilly.com/mysql2010/public/schedule/detail/13366 Poor database design is the number one cause of both database downtime and bad performance, and is especially an issue with large data warehouses. This session will teach you how to navigate down through the process of building an analytic database design that will hold up under pressure. Topics covered include: Building the logical design: -Moving from transactional to analytic schemas -Stars, snowflakes, and more -Vertical and horizontal partitioning model trade-offs -Quick note on using MySQL Workbench to design data warehouses Transitioning to the physical design: -How to decide on the right storage engine(s) -When to/not to use row and column-oriented storage engines -How to decide on a partitioning strategy -Optimizing for fast data loads -The love-hate relationship with indexes -Hardware architectures: SMP, MPP, or both? -Use case benchmarks for the above designs Monitoring and tuning the design: -Monitoring checklist (database, OS, and storage) -SQL diagnostic troubleshooting best practices -When to quit and start over
Views: 15508 tcation
Study of Database Intrusion Detection Based on Improved Association Rule Algorithm
 
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Title: Study of Database Intrusion Detection Based on Improved Association Rule Algorithm Domain: Data Mining Description: The proposed work is a hybrid approach that contains the detection of malicious and intrusive activity by combining two techniques, one is of association rule and second is Log mining. By combining these two methods we can achieve better efficiency by finding accurate intrusion in the database. The proposed method can be place on database management level and thus provide security to the database. The existing systems have limitations of missing few intrusions and high false positive rates and also they have overhead of creating profiles and keeping record of all the activities and update the large database every time. Intrusion detection technology refers to identify any activities of damage to the computer system security, integrity and confidentiality Different from the traditional operating system reinforcement, authentication and firewall security isolation technology, intrusion detection as an active dynamic security defence technologies, it provides internal attacks and external attacks and misuse in real-time protection. Data mining is an interdisciplinary field, affected by a number of disciplines, including database systems, statistics, machine learning, visualization and information science. There are many data mining methods commonly used in database intrusion detection, in which the association rule mining algorithm and sequential pattern mining algorithm are widely applied in particular. Association rule is to find the correlation of different items appeared in the same event. Association rule mining is to derive the implication relationships between data items under the conditions of a set of given project types and a number of records and through analyzing the records, the commonly used algorithm is Apriori algorithm. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2017 - 2018 48. 2017 - 2018 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
Final Year Projects | Efficient Algorithms for Mining High Utility Itemsets
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 284 Clickmyproject
Mining Human Activity Patterns From Smart Home Big Data for Health Care Applications
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 102 Clickmyproject
Lecture - 10 Storage Structures
 
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Lecture Series on Database Management System by Dr.S.Srinath, IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 52892 nptelhrd
Efficient Algorithms for Mining Top - K High  Utility Itemsets | Final Year projects 2016
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 1170 Clickmyproject
Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 112 Clickmyproject
Theoretical Foundations & Software Infrastructure for Biological Network Databases
 
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Mehmet Koyuturk is a professor at the Department of Electrical Engineering and Computer Science at Case Western Reserve University. Ananth Grama is the Samuel Conte Professor of Computer Science at Purdue University. In this web lecture, Dr. Koyuturk and Dr. Grama will present on the topic “Theoretical Foundations & Software Infrastructure for Biological Network Databases." Video Description In biomedical applications, network models are commonly used to represent interactions and higher-level associations among biological entities. Integrated analyses of these interaction and association data have proven useful in extracting knowledge and generating novel hypotheses for biomedical research. However, existing computational infrastructure for storing and querying, network data target networks at smaller scales. In this seminar, we will describe algorithms and indexing techniques that use results from classical linear algebra for fast processing of network proximity queries on very large networks. We will then demonstrate sample applications of these algorithms in the context of drug repositioning, construction of tissue-specific protein interaction networks, and single-cell transcriptomics. About the Speakers Mehmet Koyutürk received his Ph.D. degree (2006) in Computer Science from Purdue University and his B.S. (1998) and M.S. (2000) degrees from Bilkent University, respectively in Electrical Engineering and Computer Engineering. He is currently Professor at the Department of Electrical Engineering and Computer Science at Case Western Reserve University (CWRU). He also holds a secondary appointment at the Center for Proteomics and Bioinformatics at the School of Medicine. His research focuses on the analysis of biological networks, systems biology of complex diseases, and computational genomics. Mehmet is an associate editor for IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB). Ananth Grama is the Samuel Conte Professor of Computer Science at Purdue University. His areas of research include high-performance computing, large-scale data analytics, and computational biology. He received his Ph.D. from the University of Minnesota in 1996 and has been at Purdue since. View slides from this lecture: https://drive.google.com/open?id=1_u5il4yJtcxVRztjAELzM84_ACCnZTXl Visit our webpage to view archived videos covering various topics in data science: https://bigdatau.ini.usc.edu/data-science-seminars
Discovering association rules using the Apriori algorithm
 
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There are several mining algorithms of association rules. One of the most popular algorithms is Apriori that is used to extract frequent itemsets from large database and getting the association rule for discovering the knowledge. Based on this algorithm, this paper indicates the limitation of the original Apriori algorithm of wasting time for scanning the whole database searching on the frequent itemsets, and presents an improvement on Apriori by reducing that wasted time depending on scanning only some transactions. The paper shows by experimental results with several groups of transactions, and with several values of minimum support that applied on the original Apriori and our implemented improved Apriori that our improved Apriori reduces the time consumed by 67.38% in comparison with the original Apriori, and makes the Apriori algorithm more efficient and less time consuming.
Fighting Bank Fraud with Real-time Graph Database
 
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Banking organizations are having to deal with highly complex fraud rings today. Fraudsters are spreading their activities across a large number of transactions and geographical regions in a more coordinated fashion, making traditional anomaly identification methods nearly obsolete. Join us for a discussion and live demo with Jie Wu, Director of Product Marketing, DataStax and guest speaker Scott Heath, Chief Revenue Officer, Expero for a discussion on how to use real-time graph database to effectively detect fraud and reduce risk in real time. View slides: https://www.slideshare.net/DataStax/webinar-fighting-bank-fraud-with-realtime-graph-database Explore all DataStax webinars: https://www.datastax.com/resources/webinars
Views: 672 DataStax

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