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Without Expert Fuzzy based Data Mining based On Fuzzy Similarity to Mine New Association Rules
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 36 - Number 2 |
| Year of Publication: 2011 |
| Authors: Gagan Dhawan, Aakanksha Mahajan |
10.5120/4460-6248
|
Gagan Dhawan, Aakanksha Mahajan . Without Expert Fuzzy based Data Mining based On Fuzzy Similarity to Mine New Association Rules. International Journal of Computer Applications. 36, 2 ( December 2011), 1-5. DOI=10.5120/4460-6248
Abstract
The problem of mining association rules in a database are introduced. Most of association rule mining approaches aim to mine association rules considering exact matches between items in transactions. A new algorithm called “Without expert fuzzy based data mining Based on Fuzzy Similarity to mine new Association Rules” which considers not only exact matches between items, but also the fuzzy similarity between them. In this paper their should not be a requirement to have an expert for finding similarity between items. Without expert fuzzy based(WEFB) Data Mining Based on fuzzy Similarity to mine new Association Rules uses the concepts of without expert to represent the similarity degree between items, and proposes a new way of obtaining support and confidence for the association rules containing these items. The problem is to find all association rules that satisfy user-specified minimum support and minimum confidence constraints. This paper then results that new rules bring more information about the database.
References
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