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

Journal of Computer & Robotics

مقاله 6، دوره 7، شماره 1، اردیبهشت 2014، صفحه 57-66   

نویسندگان

Rasool Azimi^{* 1}؛ Hedieh Sajedi²

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

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

چکیده

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.

کلیدواژه‌ها

Data mining؛ Clustering؛ K-means؛ Persistent K-Means