An Approach to Classification Based on Fuzzy Association Rules

DOI

10.2991/iske.2007.232How to use a DOI?

Keywords

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

Abstract

Classification based on association rules is considered effective and advantageous in many cases. However, the "sharp boundary" problem in association rules mining with numerical data may lead to semantics retortion of discovered rules, which may further disturb the understandability, even the accuracy of classification. This paper is aimed at proposing an associative classification approach, namely Fuzzy Association Rules Classification (FARC), where fuzzy logic is used in partitioning the domains of numerical data items, giving rise to fuzzy association rules for classification. In doing so, two measures, pseudo support and pseudo confidence, as well as the notion of minimal equivalence set (MESet), are introduced, along with extensions to the corresponding mining algorithms. The experimental results revealed that FARC generated fewer rules than the traditional CBA approach without loss of accuracy.

Copyright

© 2007, 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  - CONF
AU  - Guoqing Chen
AU  - Zuoliang Chen
PY  - 2007/10
DA  - 2007/10
TI  - An Approach to Classification Based on Fuzzy Association Rules
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 1367
EP  - 1372
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.232
DO  - 10.2991/iske.2007.232
ID  - Chen2007/10
ER  -