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 …

The issue of determining 'the right number of clusters' is attracting ever growing interest. The
paper reviews published work on the issue with respect to mixture of distributions, partition …

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 …

The issue of determining “the right number of clusters” in K-Means has attracted
considerable interest, especially in the recent years. Cluster intermix appears to be a factor …

Clustering is a fundamental and widely used method for grou** similar records in one
cluster and dissimilar records in the different cluster. In cluster analysis, a major problem is …

The bee colony optimization (BCO) algorithm is proved to be one of the fast, robust and
efficient global search heuristics in tackling different practical problems. Considering BCO …

The multiobjective realization of the data clustering problem has shown great promise in
recent years, yielding clear conceptual advantages over the more conventional, single …

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 …

This paper elaborates on the improvement of an evolutionary algorithm for clustering (EAC)
introduced in previous work. Four new features are proposed and empirically assessed in …

In this paper we consider both hydrology and governance as critical dimensions for irrigation
water security. We scale down the overall water security concept to the agricultural sector …