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

Subject Areas : Journal of Computer & Robotics

Rasool  Azimi ¹ (Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran)
Hedieh  Sajedi ² (Department of Computer Science, College of Science, University of Tehran, Tehran, Iran)

Received: 2012-03-03 Accepted : 2012-04-10 Published : 2014-02-01

Keywords: Data mining, Clustering, K-means, Persistent K-Means,

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.