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General Information
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2011 Vol.3(2): 255-260 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2011.V3.313

Frequent Itemsets from Multiple Datasets with Fuzzy data

Praveen Arora, R. K. Chauhan and Ashwani Kush

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 praveen@jimsindia.org.
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 atakush20@gmail.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.

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