Persistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm

Authors

1 Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

2 Department of Computer Science, College of Science, University of Tehran, Tehran, Iran

Abstract

Identifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means algorithm, namely Persistent K-Means, which alters the convergence method of K-Means algorithm to provide more accurate clustering results than the K-means algorithm and its variants by increasing the clusters’ coherence. Persistent K-Means uses an iterative approach to discover the best result for consecutive iterations of K-Means algorithm.

Keywords


Volume 7, Issue 1 - Serial Number 1
February 2014
Pages 57-66
  • Receive Date: 03 March 2012
  • Revise Date: 25 March 2012
  • Accept Date: 10 April 2012
  • First Publish Date: 01 February 2014