Association rules from large Data warehouses are becoming increasingly important. In support of this trend, the paper proposes a new model for finding frequent itemsets from large databases that contain tables organized in a star schema with fuzzy taxonomic structures. The study aims to incorporate the previous developed algorithms on mining fuzzy generalized association rules and Mining Association rules in Entity relationship Models to discover a new algorithm. The paper focuses on the extraction of multi level linguistic association rules from multiple tables and examines the performance of extracted rules. An example given in the study demonstrates that the proposed mining algorithm can derive multi level fuzzy association rules from multiple datasets in a simple and effective manner.
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.
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