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
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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