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 …

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 …

This paper presents a comprehensive survey of the meta-heuristic optimization algorithms
on the text clustering applications and highlights its main procedures. These Artificial …

Clustering is the unsupervised classification of patterns (observations, data items, or feature
vectors) into groups (clusters). The clustering problem has been addressed in many contexts …

Data analysis plays an indispensable role for understanding various phenomena. Cluster
analysis, primitive exploration with little or no prior knowledge, consists of research …

Clustering is the division of data into groups of similar objects. In clustering, some details are
disregarded in exchange for data simplification. Clustering can be viewed as a data …

In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal
partition of a given data into a specified number of clusters. GA's used earlier in clustering …

This is the first book to take a truly comprehensive look at clustering. It begins with an
introduction to cluster analysis and goes on to explore: proximity measures; hierarchical …

Rule-based fuzzy modeling has been recognised as a powerful technique for the modeling
of partly-known nonlinear systems. Fuzzy models can effectively integrate information from …

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 …