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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.
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: 751 Deepika Starz
HEURISTICS RULES BASED MINING HIGH UTILITY ITEMSETS FROM TRANSACTIONAL DATABASE
 
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Mining frequent itemsets is an active area in data mining that aims at searching interesting relationships between items in databases. It can be used to address to a wide variety of problems such as discovering association rules, sequential patterns, correlations and much more. A transactional database is a data set of transactions, each composed of a set of items, called an itemset (frequently occurring in a database). Existing methods often generate a huge set of potential high utility item sets and their mining performance is degraded consequently. There is a lacking of mining performance with these huge number of potential high utility itemsets; higher processing Time too. Two novel algorithms as well as a compact data structure for efficiently discovering high utility itemsets are proposed. High utility itemsets is maintained in a tree-based data structure named UP-Tree (Utility Pattern Tree). Implementing mining process through Discarding Local Unpromising Items and Decreasing Local Node Utilities strategies. An experimental result predicts that not only reduces the number of candidates effectively but also outperforms other algorithms DIVYA BHARATHY.M (VMC 791) Department of Master of Computer Applications Veltech Multi Tech Engg College.
Views: 276 Divya Bharathy
OLTP vs. OLAP (1/2)
 
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05/22/2012 - As we explore more effective ways to access vast amount of data - BI Appliances, Big Data solutions and In Memory Analytics - this video blog explains the fundamental reasons why Relational database management system (RDBMS) which has been with us for over 3 decades are being used for a different purpose than they were designed for in Analytics and why do we need specialized solutions in some cases.
Views: 146466 SaamaInc
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: 303 Clickmyproject
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: 56396 Michael Lamont
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: 2498 Clickmyproject
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: 18836 atoz knowledge
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: 242 Clickmyproject
Transactional Data Input
 
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Apps built with Code On Time have an exclusive featured called Offline Data Processor (ODP). Update, Insert, and Delete operations performed via user interface or with custom code are "simulated" on the client and logged. Changelog is committed to the server when user saves the master record. Mobile apps also benefit from this feature. If Offline Sync Add-On is installed in the app, then data change logs are stored on the device. Offline (disconnected) app synchronizes data change logs with the server upon user request.
Views: 847 Code On Time
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: 783 myproject bazaar
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: 15 L.Mohan Arun
A Data Mining Project -- Discovering association rules using the Apriori algorithm
 
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Graduate student Jing discusses her data mining term project which uses the Apriori algorithm (market basket analysis) to mine association rules from a set of database transactions.
Views: 14563 CSDepartment St. Joes
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: 344 KDD2017 video
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: 1146 Clickmyproject
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: 1845 Shiva Kumar
apriori algorithm (data mining)
 
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********************************************* visit below link for examples http://britsol.blogspot.in/2017/08/apriori-algorithm-example.html book name : techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar ******************************************** MORE DATA MINING ALGORITHM PLAYLIST IS ON BELOW LINK: https://www.youtube.com/watch?v=JZepOmvB514&list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr
Views: 92296 fun 2 code
mod01lec01
 
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Views: 26770 Data Mining - IITKGP
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
IEEE Projects | Efficient Mining of Frequent Itemsets on Large Uncertain Databases
 
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IEEE Projects | Efficient Mining of Frequent 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 ======================= * 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: 513 Clickmyproject
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: 211029 nptelhrd
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: 29090 Google Developers
Final Year Projects | Efficient Algorithms for Mining High Utility Itemsets from Transactional
 
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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: 797 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 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: 46 Clickmyproject
DBMS - Granularity of Data Items
 
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DBMS - Granularity of Data Items Watch more Videos at https://www.tutorialspoint.com/videotutorials/index.htm Lecture By: Mr. Arnab Chakraborty, Tutorials Point India Private Limited
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: 85051 StudyKorner
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: 5000 myproject bazaar
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://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: 26 myproject bazaar
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: 66 Clickmyproject
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: 107 SHPINE TECHNOLOGIES
What the Heck is an In Memory Data Grid | DataEngConf SF '18
 
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Don’t miss the next DataEngConf in Barcelona: https://dataeng.co/2O0ZUq7 Download Slides: https://dataeng.co/2xs0glw ABOUT THE TALK: In-Memory Data Grids (IMDGs) are the backbone of some of the most data-intensive workloads in the world. If you are booking travel, making a stock trade, or buying a home, chances are an IMDG is involved. This talk will focus on the architecture of In-Memory Data Grids by diving into the internals of Apache Geode, a popular, open-source IMDG. Through understanding the internal architecture and characteristic of these systems we will discover the data engineering problems they solve, and when / when not to use them. We will also get hands on with Apache Geode and see how it can be used to speed up a legacy relational database. ABOUT THE SPEAKER: Addison Huddy is a contributor to Apache Geode and a member of the Research & Development team at Pivotal Software. Prior to Pivotal, Addison worked at Visa where he developed some of Visa's early mobile wallets, as well as, data-driven advertising products. Addison has a Bachelors from UCLA and a Masters in Computer Science from the Georgia Tech. Follow DataEngConf on: Twitter: https://twitter.com/dataengconf LinkedIn: https://www.linkedin.com/company/hakkalabs Facebook: https://web.facebook.com/hakkalabs
Views: 925 Data Council
NOVEL  SECURE MULTI PARTY ALGORITHMS OR HORIZONTALLY DISTRIBUTED DATABASE IN FAST DISTRIBUTED MINING
 
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This Project proposes a protocol for secure mining of association rules in horizontally distributed databases.
How to Build a Fraud Detection Solution with Neo4j
 
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Joe Depeau, Neo4j
Views: 1517 Neo4j
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: 2513113 Simply Explained - Savjee
Business Intelligence: Data Warehouses
 
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A basic introduction to data warehouses, their uses, and their benefits. Downloadable slides available from SlideShare at http://goo.gl/0qksld
Views: 822 Michael Lamont
Single View of Data :: Best-in-Class Matching
 
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A cleansed single view of master data is the foundation for ensuring accuracy and consistency across business transactions, reports and analysis. Maestro’s matching engine delivers best-in-class accuracy, along with myriad tools to finesse and fine-tune results – all with no coding or scripting required.
Views: 1576 Profisee
Hey Relational Developer, Let's Go Crazy (Patrick McFadin, DataStax) | Cassandra Summit 2016
 
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Slides: https://www.slideshare.net/DataStax/hey-relational-developer-lets-go-crazy-patrick-mcfadin-datastax-cassandra-summit-2016 | You've made a good career developing applications using a relational database. You know learning how to be a Cassandra developer is going to be a great skill to add. Now it's time to bridge those two things into reality. I was in your shoes and I can help. How do you work without ACID transactions? The data model looks similar but is so different! What are some of the bad things I should avoid? What are some of the traps I can fall into moving from a relational database? I hear these questions all the time. Let's spend some time to walk through each one and get you on track. Before you know it, you'll be going crazy on your next Cassandra based application! About the Speaker Patrick McFadin Chief Evangelist, DataStax Patrick McFadin is one of the leading experts of Apache Cassandra and data modeling techniques. As the Chief Evangelist for Apache Cassandra and consultant for DataStax, he has helped build some of the largest and exciting deployments in production. Previous to DataStax, he was Chief Architect at Hobsons and an Oracle DBA/Developer for over 15 years.
Views: 1514 DataStax
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
Joins in sql server in arabic
 
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Text version of the video http://csharp-video-tutorials.blogspot.com/2012/08/joins-in-sql-server-part-12.html Slides http://csharp-video-tutorials.blogspot.com/2013/08/part-12-joins.html All SQL Server Text Articles http://csharp-video-tutorials.blogspot.com/p/free-sql-server-video-tutorials-for.html All SQL Server Slides http://csharp-video-tutorials.blogspot.com/p/sql-server.html All Dot Net and SQL Server Tutorials in English https://www.youtube.com/user/kudvenkat/playlists?view=1&sort=dd All Dot Net and SQL Server Tutorials in Arabic https://www.youtube.com/c/KudvenkatArabic/playlists
Views: 1626 kudvenkat.arabic
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: 103 Clickmyproject
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: 115 LeMeniz Infotech
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: 52563 nptelhrd
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: 274 Clickmyproject
Final Year Projects 2016 | A decision-theoretic rough set approach for dynamic data mining
 
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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: 122 Clickmyproject
Final Year Projects | Mining frequent patterns from dynamic data stream
 
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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: 171 myproject bazaar
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: 46672 Augmented Startups
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
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://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: 797 myproject bazaar

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