Technically, the problem of overlap in a dataset is viewed as an uncertainty problem and
is solved using a fuzzy set theoretical approach, specifically, fuzzy clustering. This
approach is powerful but has some problems associated with it, of which the design of the
membership function is the most serious. There are many different techniques for
optimizing fuzzy clustering, including those based on similarity decomposition and
centroids of clusters. Furthermore, the problem of overlap clustering is still being studied
to improve its performance, especially with respect to the membership optimization.
Rough set theory (RST) is the complement of fuzzy set theory and evidence theory,
which use different techniques to address the uncertainty problem in overlap clustering.
Considering the simplicity of the membership computation in RST, we propose an
overlap clustering algorithm, which involves the use of the discernibility concept of RST
to improve the overlap clusters as an existing variant of the overlap clustering algorithm.
The experiment described here demonstrates that this new method improves the
performance and increases the accuracy of clustering while avoiding the time complexity
problem. The experiment uses five UCI machine learning datasets. The complexity of the
data is measured using the volume of the overlap region and feature efficiency. The
experimental results show that the proposed method significantly outperforms the other
two methods in terms of the Dunn index, the sum of the squared errors and the silhouette
index.