doi: 10.7763/IJCTE.2010.V2.253
Association Rule Mining for Multiple Tables With Fuzzy Taxonomic Structures
Praveen Arora
,
R. K. Chauhan2 ,
Ashwani Kush3
- 2rkc.dcsa@gmail.com
- 3university college, Kurukshetra University India.
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.
Keywords
- Association Rules
- Data Mining
- Fuzzy data
- ER models
How to 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. https://doi.org/10.7763/IJCTE.2010.V2.253
Copyright & License
Copyright © 2010 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).