The partitional clustering concept started with K-means algorithm which was published in
1957. Since then many classical partitional clustering algorithms have been reported based …

Image segmentation is an essential phase of computer vision in which useful information is
extracted from an image that can range from finding objects while moving across a room to …

This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries
to reflect the profile of this area by focusing more on those subjects that have been given …

Delineating the tremendous growth in this area, the Handbook of Approximation Algorithms
and Metaheuristics covers fundamental, theoretical topics as well as advanced, practical …

The synergy between data security and high intensive computing has envisioned the way to
robust anomaly detection schemes which in turn necessitates the need for efficient data …

Clustering is an important unsupervised classification technique where data points are
grouped such that points that are similar in some sense belong to the same cluster. Cluster …

One of the top ten most influential data mining algorithms, k-means, is known for being
simple and scalable. However, it is sensitive to initialization of prototypes and requires that …

Clustering is a useful exploratory tool for gene-expression data. Although successful
applications of clustering techniques have been reported in the literature, there is no method …

Data clustering is an unsupervised data analysis and data mining technique, which offers
refined and more abstract views to the inherent structure of a data set by partitioning it into a …

Many clustering algorithms categorized as K-clustering algorithm require the user to predict
the number of clusters (K) to do clustering. Due to lack of domain knowledge an accurate …