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PostMining of weighted assosiation rules using knowledge base
 

PostMining of weighted assosiation rules using knowledge base

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    PostMining of weighted assosiation rules using knowledge base PostMining of weighted assosiation rules using knowledge base Presentation Transcript

    • Guided by, M.R.Vinu Ms. Nandhini. M, 11MZ36 Asst Professor(Sr. Gr), II M.E CSE Dept of CSE, Dept of CSE PSG College of Technology. PSG College of Technology.
    • Agenda  Objective  Problem Definition  Literature Survey  Need for Current Study  Methodology  Scope of the Project  Timeline of Activities  Major References
    • What is Association Rule Mining and Weighted Association rule mining?  Association rule mining is popular method, to mine association rules that satisfy the predefined minimum support and confidence from a given database.  In weighted association rule mining, weight are assigned to each item to reflect interest of each item within the transaction.
    • OBJECTIVES  To improve the confidence of the rule using pre assigned weights for each item/attribute.  To improve the quality of the rule by reducing the quantity of the rules without compromising the usefulness factor.  To improve the computational efficiency of rule mining.
    • LITERATURE SURVEY
    • Association Rule Induction Apriori algorithm is the best known algorithm to mine association rules but ends up with many disadvantages  The algorithm does not return result in a reasonable time.  It only tells the presence and absence of an item in transactional database.  It is not efficient in case of large dataset(dense dataset).  ARM fails to associate subjective measures.
    • Contd..  So, the traditional model of association rule mining is adapted to handle weighted association rule mining.  This algorithm is both scalable and efficient in discovering significant relationships but still ends up in long frequent pattern for dense data.  This problem does not require changes in the existing rule mining ,therefore it is necessary to apply during post- mining of association rules.
    • PROBLEM DEFINITION • It is difficult for the users to find interesting association rule as there are more number of association rules discovered before. • Therefore, it is important to remove insignificant rules and prune redundancy as well as summarize, visualize, and post-mine the discovered rules
    • METHODOLOGY
    • SCOPE OF THE PROJECT  Generating Weighted Association Rules  Creating ontology for the Datasets(Census Income Dataset)  Generating Rule Schemas  Applying Operators over rule schemas  Applying Filters over rules schemas  Comparing efficiency after applying operators and Filters
    • NEED FOR CURRENT STUDY  Weighted concept is used in order to gain a more realistic mining result.  Weighted Association Rules can filter out the minor rules, extracts the implicit and useful rules for users.
    • TIMELINE ACTIVITIES
    • Activities June July August September I II III IV I II III IV I II III IV I II III IV Literature review, Data collection. Creating Ontology for the dataser Generating Rule Schemas Applying Operators over rule schemas and Filters over rules schemas Comparing efficiency Work planned Work done Work in progress
    • MAJOR REFERENCES  C.H.Cai ,Ada W.C.Fu ,C.H.Cheng and W.W. Kwong “Mining Association Rules with weighted Itemset”.In:Department of computer science and Engineering The Chinese University of Hong Kong  R. Natarajan and B. Shekar, “A Relatedness-Based Data-Driven Approach to Determination of Interestingness of Association Rules,” Proc. 2005 ACM Symp. Applied Computing (SAC), pp. 551-552, 2005.  R.J. Bayardo, Jr., R. Agrawal, and D. Gunopulos, “Constraint-Based Rule Mining in Large, Dense Databases,” Proc. 15th Int’lConf. Data Eng. (ICDE ’99), pp. 188-197, 1999.
    • Contd…  Wei Wang,Jiong Yang,Philip S.Yu “Efficient Mining Of weighted Association Rules”,In:ACM 2000  Claudia Marinica anf Fabricegillet,june2010,”Knowlede –Based interactive Postmining of Association Rules using Ontologyies”.In :IEEE Transaction on knowledge and Data Engineering
    • THANKYOU