Clustering with XCS and agglomerative rule merging

Authors:
Liangdong Shi;Yinghuan Shi;Yang Gao
Affiliations:
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China
Venue:
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Year:
2009

Citing 8
Cited 0

Chameleon: Hierarchical Clustering Using Dynamic Modeling

Computer
Compact Rulesets from XCSI

IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Accuracy-based learning classifier systems: models, analysis and applications to classification tasks

Evolutionary Computation
NOCEA: A rule-based evolutionary algorithm for efficient and effective clustering of massive high-dimensional databases

Applied Soft Computing
Learning classifier systems

Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Towards clustering with XCS

Proceedings of the 9th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy

Evolutionary Computation
Learning classifier system ensemble and compact rule set

Connection Science - Evolutionary Learning and Optimisation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a more effective approach to clustering with eXtended Classifier System (XCS) which is divided into two phases. The first phase is the XCS learning process with rule compact, during which we alter the XCS mechanisms and propose a new way to calculate rewards. After learning, the rules are evolved to form the final population consisting of rules with homogeneous data distribution. The second phase is merging the learnt rules to generate final clusters. We achieve this by modelling the rules as sub-graphs and merging the subgraphs based on some criteria similar to CHAMELEON. Experimental results validate the effectiveness on a number of datasets, which contain clusters of different shapes, densities and distances.