Azimi, R., Sajedi, H. (2014). Persistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm. Journal of Computer & Robotics, 7(1), 57-66.
Rasool Azimi; Hedieh Sajedi. "Persistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm". Journal of Computer & Robotics, 7, 1, 2014, 57-66.
Azimi, R., Sajedi, H. (2014). 'Persistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm', Journal of Computer & Robotics, 7(1), pp. 57-66.
Azimi, R., Sajedi, H. Persistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm. Journal of Computer & Robotics, 2014; 7(1): 57-66.
Persistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm
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
Receive Date: 03 March 2012,
Revise Date: 25 March 2012,
Accept Date: 10 April 2012
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.
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