In this paper, we propose a novel approach to clustering noisy and complex data sets based
on the eXtend Classifier Systems (XCS). The proposed approach, termed XCSc, has three …

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 …

A density clustering method based on eXtended Classifier Systems (XCS) was proposed,
which could be used to cluster the two-dimensional data sets with arbitrary shapes and …

Abstract Learning Classifier System (LCS) is an effective tool to solve classification
problems. Clustering with XCS (accuracy-based LCS) is a novel approach proposed …

In this paper, we present a multiple data clusterings combiner, based on a proposed
Weighted Shared nearest neighbors Graph.(WSnnG). While combining of multiple classifiers …

Clustering ensemble has received considerable research interest and led to a proliferation
of studies, since it has great capabilities to combine multiple base clusters to generate a …

Clustering plays a pivotal role in knowledge processing, knowledge bases, and expert
systems, enabling AI systems to acquire knowledge effectively. Hierarchical clustering, in …

Ensemble clustering has emerged as a combination of several basic clustering algorithms to
achieve high quality final clustering. However, this technique is challenging due to the …

Bagging and boosting are two well-known methods of developing classifier ensembles. It is
generally agreed that the clusterer ensemble methods that utilize the boosting concept can …

CURE (clustering using representatives) is an efficient clustering algorithm for large
databases, which is more robust to outliers compared with other clustering methods, and …