Motivation Many real applications such as businesses and health generate large categorical
datasets with uncertainty. A fundamental task is to efficiently discover hidden and non-trivial …

Clustering techniques are used to split data into clusters where each cluster contains
elements that look more similar to elements in the same cluster than elements in other …

Clustering a set of objects into homogeneous groups is a fundamental operation in data
mining. Recently, many attentions have been put on categorical data clustering, where data …

Clustering a set of data into homogeneous groups is a fundamental operation in data
mining. Recently, consideration has been put on categorical data clustering, where the data …

Categorical data is different from continuous data, where the values of attribute do not follow
any natural ordering. Moreover, inherent complexities like uncertainty, vagueness and …

Clustering techniques are the unsupervised data mining applications and are important in
data mining methods for exploring natural structure and identifying interesting patterns in …

Lots of clustering algorithms have been developed, while most of them cannot process
objects in hybrid numerical/nominal attribute space or with missing values. In most of them …

The aim of this study is focusing the issue of traditional clustering algorithm subjects to data
space distribution influence, a novel clustering algortihm combined with rough set theory is …

Representing the positive, possible, and boundary regions of clusters, rough set-based C-
means clustering methods, such as generalized rough C-means (GRCM) and rough set C …

A variety of cluster analysis techniques exist to group objects having similar characteristics.
However, the implementation of many of these techniques is challenging due to the fact that …