Volume 1, Issue 3, August 2008, Pages 262 - 272
Building an Associative Classifier Based on Fuzzy Association Rules
Zuoliang Chen, Guoqing Chen
Received 11 February 2008, Revised 30 June 2008, Available Online 1 August 2008.
- https://doi.org/10.2991/ijcis.2008.1.3.7How to use a DOI?
- Associative Classification, Fuzzy Association rules, CFAR, Data Mining.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Zuoliang Chen AU - Guoqing Chen PY - 2008 DA - 2008/08 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 - https://doi.org/10.2991/ijcis.2008.1.3.7 ID - Chen2008 ER -