Special Issue on Cluster Analysis

Cluster Analysis (or clustering) is the subfield of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is typically used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. Cluster analysis can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. It will often be necessary to modify data preprocessing and model parameters until the result achieves the desired properties. The goal of this special issue is to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in this area of cluster analysis.

In this special issue, we intend to invite front-line researchers and authors to submit original researches and review articles on exploring cluster analysis. Potential topics include, but are not limited to:  

Authors should read over the journal’s Authors’ Guidelines carefully before submission. Prospective authors should submit an electronic copy of their complete manuscript through the journal’s Paper Submission System.