Cluster analysis is an essential tool in data mining. Several clustering algorithms have been
proposed and implemented, most of which are able to find good quality clustering results …

Clustering is a popular data analysis and data mining technique. A popular technique for
clustering is based on k-means such that the data is partitioned into K clusters. However, the …

Clustering techniques have received attention in many fields of study such as engineering,
medicine, biology and data mining. The aim of clustering is to collect data points. The K …

The symbiotic organism search (SOS) is a recently proposed metaheuristic optimization
algorithm that simulates the symbiotic interaction strategies adopted by organisms to survive …

Clustering is a popular data-analysis and data-mining technique that has been addressed in
many contexts and by researchers in many disciplines. The K-means algorithm is one of the …

Data clustering is one of the main issues in the optimization problem. It is the process of
clustering a group of items into several groups. Items within each group have the greatest …

Clustering is a popular data analysis and data mining technique. The k-means clustering
algorithm is one of the most commonly used methods. However, it highly depends on the …

In cluster analysis, the goal has always been to extemporize the best possible means of
automatically determining the number of clusters. However, because of lack of prior domain …

The K-means algorithm is one of the most popular techniques in clustering. Nevertheless,
the performance of the K-means algorithm depends highly on initial cluster centers and …

Membrane computing is a class of distributed parallel computing models. Inspired from the
structure and inherent mechanism of membrane computing, a membrane clustering …