Contact - 08975313145
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Views: 1113 nitin ujgare
Final Year Projects | An IntrusionDetection Model Based on Fuzzy Class-Association-Rule Mining Using Genetic Network Progr 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: 617 Clickmyproject
This is DATA MINING Domain. Developped In Java Platform. Developper: Vedha Technologies. Contact: 9500012060
Views: 1681 Vedha Technologies
For More Explanation And Techniques Contact:K.Manjunath,9535866270, http://www.tmksinfotech.com Bangalore,Karnataka.
Views: 997 manju nath
The mechanism for unearthing hidden facts in large datasets and drawing inferences on how a subset of items influences the presence of another subset is known as Association Rule Mining (ARM). There is a wide variety of rule interestingness metrics that can be applied in ARM. Due to the wide range of rule quality metrics it is hard to determine which are the most `interesting' or `optimal' rules in the dataset. In this paper we propose a multi-objective approach to generating optimal association rules using two new rule quality metrics: syntactic superiority and transactional superiority. These two metrics ensure that dominated but interesting rules are returned to not eliminated from the resulting set of rules.
Views: 455 Final Year Solutions
-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 37158 Well Academy
#kmean 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: 354898 Last moment tuitions
To access the code go to the Machine Learning Tutorials Section on the Tutorials page here: http://www.brunel.ac.uk/~csstnns Using WEKA in java
Views: 16249 Noureddin Sadawi
Take the Full Course of Artificial Intelligence What we Provide 1) 28 Videos (Index is given down) 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in Artificial Intelligence Sample Notes : https://goo.gl/aZtqjh To buy the course click https://goo.gl/H5QdDU if you have any query related to buying the course feel free to email us : [email protected] Other free Courses Available : Python : https://goo.gl/2gftZ3 SQL : https://goo.gl/VXR5GX Arduino : https://goo.gl/fG5eqk Raspberry pie : https://goo.gl/1XMPxt Artificial Intelligence Index 1)Agent and Peas Description 2)Types of agent 3)Learning Agent 4)Breadth first search 5)Depth first search 6)Iterative depth first search 7)Hill climbing 8)Min max 9)Alpha beta pruning 10)A* sums 11)Genetic Algorithm 12)Genetic Algorithm MAXONE Example 13)Propsotional Logic 14)PL to CNF basics 15) First order logic solved Example 16)Resolution tree sum part 1 17)Resolution tree Sum part 2 18)Decision tree( ID3) 19)Expert system 20) WUMPUS World 21)Natural Language Processing 22) Bayesian belief Network toothache and Cavity sum 23) Supervised and Unsupervised Learning 24) Hill Climbing Algorithm 26) Heuristic Function (Block world + 8 puzzle ) 27) Partial Order Planing 28) GBFS Solved Example
Views: 226196 Last moment tuitions
Contact - 08975313145
Views: 138 Codeengine
What are association rules?; Operationalizing rules; Association rules vs. collaborative filtering; Antecedent and consequent; Frequent itemsets and the concept of Support; The Apriori algorithm This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: http://www.dataminingbook.com https://www.twitter.com/gshmueli https://www.facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Networks: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 534 Galit Shmueli
Rough Set Theory | Indiscernibility | Set Approximation | Solved Example Rough Set Theory,Its Applications. Basic Concepts of Rough Sets. What is information Systems. How to find Indiscernibility. How to find Lower, Upper and Boundary Approximation of a Set.
Views: 1417 btech tutorial
In this video, we find the best reduct in an information system using rough set attribute selection
Views: 6950 Laurel Powell
Contoh aplikasi pendukung keputusan (SPK) metode association rules menggunakan php. Source code aplikasi SPK metode association rules. Download source code : http://sourcecodeaplikasi.info/download-source-code-aplikasi-spk-metode-association-rules/ Music : www.bensound.com
Views: 566 Ahmad Code
Big Data: Fuzzy Logic- In this lesson, Pratiksha Tripathi has explained about the overview of the big data fuzzy logic. She has also discussed some of the important features of Big data. Big Data is extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.Many companies are using big data to analyse their trend. Hence this lesson will also help you in increasing your employment opportunities. You can watch the full list of courses and start discussions with the educator here: https://goo.gl/xJXbkk For more educational lessons by top educators download the Unacademy Learning App from Android Playstore: https://play.google.com/store/apps/details?id=com.unacademyapp&hl=en or visit http://unacademy.com
Views: 1096 Unacademy - Programming
Association Rules are a quick and simple technique to identify groupings of products that are often sold together. This makes them useful for identifying products that could be grouped together in cross-sell campaigns. Association rules are also known as Market Basket Analysis, as they used to analyse a virtual shopping baskets. In this tutorial I will demonstrate how to create association rules with the Excel data mining addin that allows you to leverage the predictive modelling algorithms within SQL Server Analysis Services. Sample files that allow you follow along with the tutorial are available from my website at http://www.analyticsinaction.com/associationrules/ I also have a comprehensive 60 minute T-SQL course available at Udemy : https://www.udemy.com/t-sql-for-data-analysts/?couponCode=ANALYTICS50%25OFF
Views: 7809 Steve Fox
More Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 4: Learning association rules http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/nK6fTv https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 13383 WekaMOOC
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM ,EMAIL:[email protected] NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
Views: 19 NEXGEN TECHNOLOGY
In this tutorial of “Matlab implementation of disease prediction using data mining techniques.” I have shown that how a disease can be predicted by an artificial intelligence system. To make the system able to predict disease we first create the database with all accurate report from the ratio of the level of the different substance in the body by which any disease spread in the whole body. When we give input data to the system then find how much ratio is in that individual body and for which disease it matches. Then the system predicts that which disease you have and if no match found with the database then it shows you do not have any disease. If you have any query, please contact us at 8146105825 or mail us at http://www.researchinfinitesolutions.com/contact-us
Views: 4971 Fly High with AI
Classification of Frequent Itemsets using Strong Association rules which are generated by using the Apriori Algorithm.
Views: 642 Swathi Kadaru
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: 111 SHPINE TECHNOLOGIES
What is STRUCTURE MINING? What does STRUCTURE MINING mean? STRUCTURE MINING meaning - STRUCTURE MINING definition - STRUCTURE MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Structure mining or structured data mining is the process of finding and extracting useful information from semi-structured data sets. Graph mining, sequential pattern mining and molecule mining are special cases of structured data mining. The growth of the use of semi-structured data has created new opportunities for data mining, which has traditionally been concerned with tabular data sets, reflecting the strong association between data mining and relational databases. Much of the world's interesting and mineable data does not easily fold into relational databases, though a generation of software engineers have been trained to believe this was the only way to handle data, and data mining algorithms have generally been developed only to cope with tabular data. XML, being the most frequent way of representing semi-structured data, is able to represent both tabular data and arbitrary trees. Any particular representation of data to be exchanged between two applications in XML is normally described by a schema often written in XSD. Practical examples of such schemata, for instance NewsML, are normally very sophisticated, containing multiple optional subtrees, used for representing special case data. Frequently around 90% of a schema is concerned with the definition of these optional data items and sub-trees. Messages and data, therefore, that are transmitted or encoded using XML and that conform to the same schema are liable to contain very different data depending on what is being transmitted. Such data presents large problems for conventional data mining. Two messages that conform to the same schema may have little data in common. Building a training set from such data means that if one were to try to format it as tabular data for conventional data mining, large sections of the tables would or could be empty. There is a tacit assumption made in the design of most data mining algorithms that the data presented will be complete. The other necessity is that the actual mining algorithms employed, whether supervised or unsupervised, must be able to handle sparse data. Namely, machine learning algorithms perform badly with incomplete data sets where only part of the information is supplied. For instance methods based on neural networks. or Ross Quinlan's ID3 algorithm. are highly accurate with good and representative samples of the problem, but perform badly with biased data. Most of times better model presentation with more careful and unbiased representation of input and output is enough. A particularly relevant area where finding the appropriate structure and model is the key issue is text mining. XPath is the standard mechanism used to refer to nodes and data items within XML. It has similarities to standard techniques for navigating directory hierarchies used in operating systems user interfaces. To data and structure mine XML data of any form, at least two extensions are required to conventional data mining. These are the ability to associate an XPath statement with any data pattern and sub statements with each data node in the data pattern, and the ability to mine the presence and count of any node or set of nodes within the document. As an example, if one were to represent a family tree in XML, using these extensions one could create a data set containing all the individuals in the tree, data items such as name and age at death, and counts of related nodes, such as number of children. More sophisticated searches could extract data such as grandparents' lifespans etc. The addition of these data types related to the structure of a document or message facilitates structure mining.
Views: 426 The Audiopedia
For Full Course Experience Please Go To http://mentorsnet.org/course_preview?course_id=1 Full Course Experience Includes 1. Access to course videos and exercises 2. View & manage your progress/pace 3. In-class projects and code reviews 4. Personal guidance from your Mentors
Views: 1123 Oresoft LWC
Privacy Preserving Outsourced Association Rule Mining on Vertically Partitioned Databases 2016-2017 MICANS INFOTECH offers Projects in CSE ,IT, EEE, ECE, MECH , MCA. MPHIL , BSC, in various domains JAVA ,PHP, DOT NET , ANDROID , MATLAB , NS2 , EMBEDDED , VLSI , APPLICATION PROJECTS , IEEE PROJECTS. CALL : +91 90036 28940 +91 94435 11725 [email protected] WWW.MICANSINFOTECH.COM COMPANY PROJECTS, INTERNSHIP TRAINING, MECHANICAL PROJECTS, ANSYS PROJECTS, CAD PROJECTS, CAE PROJECTS, DESIGN PROJECTS, CIVIL PROJECTS, IEEE MCA PROJECTS, IEEE M.TECH PROJECTS, IEEE PROJECTS, IEEE PROJECTS IN PONDY, IEEE PROJECTS, EMBEDDED PROJECTS, ECE PROJECTS PONDICHERRY, DIPLOMA PROJECTS, FABRICATION PROJECTS, IEEE PROJECTS CSE, IEEE PROJECTS CHENNAI, IEEE PROJECTS CUDDALORE, IEEE PROJECTS IN PONDICHERRY, PROJECT DEVELOPMENT CENTRE
Views: 144 MICANS INFOTECH IEEE PROJECT PPT VIDEOS 2016
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
Views: 425 InnovationAdsOfIndia
This video contains detailed explanation about k-means clustering algorithm with each step explained along with a primary conclusion. Please Like and Subscribe !!!! For more videos visit Innovation Heights I.T/Data Mining Numericals Playlist.
Views: 955 Innovation Heights I.T
This video will explain List of different Machine learning Algorithm and short introduction of each one. Learning Style way : Supervised Learning Unsupervised Learning Similarity : Instance-based Regression Regularization Decision Tree Algorithms Bayesian Algorithms Clustering Algorithms Association Rule Learning Algorithms Neural Network Algorithms Dimensionality Reduction Deep Learning Ensemble Algorithms NPL, Genetic, Recommender system, Graphical Models Supervised learning : Linear Regression Logistic Regression Neural Network Algorithm Deep Learning Unsupervised Learning : K-Means Apriori algorithm Hierarchical Clustering Thank You Reference : http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
Views: 2258 MyStudy
What is INCREMENTAL LEARNING? What does INCREMENTAL LEARNING mean? INCREMENTAL LEARNING meaning - INCREMENTAL LEARNING definition - INCREMENTAL LEARNING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ In computer science, incremental learning is a method of machine learning, in which input data is continuously used to extend the existing model's knowledge i.e. to further train the model. It represents a dynamic technique of supervised learning and unsupervised learning that can be applied when training data becomes available gradually over time or its size is out of system memory limits. Algorithms that can facilitate incremental learning are known as incremental machine learning algorithms. Many traditional machine learning algorithms inherently support incremental learning, other algorithms can be adapted to facilitate this. Examples of incremental algorithms include decisions trees (IDE4, ID5R), decision rules, artificial neural networks (RBF networks, Learn++, Fuzzy ARTMAP, TopoART, and IGNG) or the incremental SVM. The aim of incremental learning is for the learning model to adapt to new data without forgetting its existing knowledge, it does not retrain the model. Some incremental learners have built-in some parameter or assumption that controls the relevancy of old data, while others, called stable incremental machine learning algorithms, learn representations of the training data that are not even partially forgotten over time. Fuzzy ART and TopoART are two examples for this second approach. Incremental algorithms are frequently applied to data streams or big data, addressing issues in data availability and resource scarcity respectively. Stock trend prediction and user profiling are some examples of data streams where new data becomes continuously available. Applying incremental learning to big data aims to produce faster classification or forecasting times.
Views: 1864 The Audiopedia
File dapat di download pada link dibawah ini https://www.dropbox.com/s/ty8limcet4optfb/Data%20Mining.rar?dl=0
Views: 1106 Ahya Ulumuddin