Building an Associative Classifier Based on Fuzzy Association Rules

DOI

10.2991/ijcis.2008.1.3.7How to use a DOI?

Keywords

Associative Classification, Fuzzy Association rules, CFAR, Data Mining.

Abstract

Classification based on association rules is considered to be effective and advantageous in many cases. However, there is a so-called "sharp boundary" problem in association rules mining with quantitative attribute domains. This paper aims at proposing an associative classification approach, namely Classification with Fuzzy Association Rules (CFAR), where fuzzy logic is used in partitioning the domains. In doing so, the notions of support and confidence are extended, along with the notion of compact set in dealing with rule redundancy and conflict. Furthermore, the corresponding mining algorithm is introduced and tested on benchmarking datasets. The experimental results revealed that CFAR generated better understandability in terms of fewer rules and smother boundaries than the traditional CBA approach while maintaining satisfactory accuracy.

Copyright

© 2008, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Zuoliang Chen
AU  - Guoqing Chen
PY  - 2008
DA  - 2008/08/01
TI  - Building an Associative Classifier Based on Fuzzy Association Rules
JO  - International Journal of Computational Intelligence Systems
SP  - 262
EP  - 272
VL  - 1
IS  - 3
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2008.1.3.7
DO  - 10.2991/ijcis.2008.1.3.7
ID  - Chen2008
ER  -