Clustering, a fundamental technique in machine learning, plays a pivotal role in partitioning
datasets into homogeneous groups. Traditional clustering algorithms, while widely adopted …

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 …

In the field of cluster analysis, rough set-based extensions of hard C-means (HCM; k-means)
including rough C-means (RCM), rough set C-means (RSCM), and rough membership C …

Abstract Developing Data Clustering algorithms have been pursued by researchers since
the introduction of k-means algorithm (Macqueen 1967; Lloyd 1982). These algorithms were …

Data clustering algorithmic schemes receive much careful research insight due to the
prominent role that clustering plays during data analysis. Proper data clustering reveals data …

ABSTRACT K-Means is an unsupervised method partitions the input space into clusters. K-
Means algorithm has a weakness of detecting outliers, which have it available in many …

In the present day scenario, there are a large number of clustering algorithms available, to
group objects having similar characteristics. But, the implementation of most of these …

Cluster analysis is a very important data mining technology, and also a hot issue in data
mining research. Among many data types to be clustered, mixed attribute data is the most …

Healthcare data clustering plays a vital role in discovering meaningful patterns and insights
from large and complex datasets. However, the boundary overlap of existing rough set …

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 …