Abstract—Most of the existing data mining algorithms handle databases consisting of single table to find association rules an large databases. Few algorithms work on multiple tables having fuzzy data with taxonomic structures. This paper proposes ‘Multi level Fuzzy rules for ER Models’ algorithm. The study focuses on the issue of mining association rules in databases having multiple levels containing fuzzy data with taxonomy and tables to be designed using Entity-Relationship (ER) Models. The study aims to incorporate the previous developed algorithms Extended Apriori and Apriori star to a new algorithm. The study will help in standardizing algorithms for finding appropriate results from database tables containing data with fuzzy taxonomic structures.
Index Terms—Association Rules, Data Mining, Fuzzy data, ER models
Parveen is working at Jagan Nath Institute of Mgmt. Sciences, Delhi, India. She can be reached at praveen@jimsindia.org. Phone
Ram Kumar is in DCSA, Kuurkshetra University Kurukshetra India. He is Chairman and Professor and can be reached at rkc.dcsa@gmail.com
Ashwani Kush is in Computer Science department at university college, Kurukshetra University India. He can be reached at akush20@gmail.com
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Cite: Praveen Arora, R. K. Chauhan and Ashwani Kush , "Association Rule Mining for Multiple Tables With Fuzzy Taxonomic Structures,"
International Journal of Computer Theory and Engineering vol. 2, no. 6, pp. 866-870, 2010.
