Abstract—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.
Index Terms—Association rules, Data Mining, Fuzzy Data, ER Models
Praveen is working at Jagan Nath Institute of Mgmt. Sciences, Delhi, India. She can be reached at email@example.com.
Ram Kumar is in DCSA, Kuurkshetra University Kurukshetra India. He is Chairman and Professor and can be reached at firstname.lastname@example.org
Ashwani Kush is in Computer Science department at university college, Kurukshetra University India. He can be reached email@example.com
Cite: Praveen Arora, R. K. Chauhan and Ashwani Kush, "Frequent Itemsets from Multiple Datasets with Fuzzy data," International Journal of Computer Theory and Engineering vol. 3, no. 2, pp. 255-260, 2011.