Effective Association rule mining using Fuzzy Apriori and Weighted Fuzzy Apriori

Vedula Venkateswara Rao; Eedala Rambabu; G. Sriramganesh

A novel approach is presented for effectively mining frequent Item sets and association rules (ARs) based on fuzzy Apriori and weighted fuzzy Apriori. The authors address the issue of invalidation of downward closure property (DCP) in weighted association rule mining where each item is assigned a weight according to its significance With respective to some user defined criteria. Most works on weighted association rule mining do not address the downward closure property while some make assumptions to validate the property. This chapter generalizes the weighted association rule mining problem with binary and fuzzy attributes with weighted settings. Their methodology follows an Apriori approach but employs T-tree data structure to improve efficiency of counting itemsets. The authors’ approach avoids pre and post processing as opposed to most weighted association rule mining algorithms, thus eliminating the extra steps during rules generation. We generalize the problem of downward closure property and propose a fuzzy weighted support and Confidence framework for Boolean and quantitative items with weighted settings. The problem of invalidation of the DCP is solved using an improved model of weighted support and confidence framework for classical and fuzzy association rule mining. The proposed approach uses both binary data and fuzzy data and generates Frequent Item Sets. This paper presents experimental results on both synthetic and real-data sets and a discussion on evaluating the proposed approach

Select Volume / Issues:

• Volume 3 Issue 3, May 2012

Year:

2012

Type of Publication:

Article

Keywords:

Frequent Item sets; Association rules; fuzzy; weighted support; weighted confidence; downward closure

Journal:

IJECCE

Volume:

3

Number:

3

Pages:

537-542

Month:

May