@article {
author = {Azimi, Rasool and Sajedi, Hedieh},
title = {Persistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm},
journal = {Journal of Computer & Robotics},
volume = {7},
number = {1},
pages = {57-66},
year = {2014},
publisher = {Qazvin Islamic Azad University},
issn = {2345-6582},
eissn = {2538-3035},
doi = {},
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 clustersâ€™ coherence. Persistent K-Means uses an iterative approach to discover the best result for consecutive iterations of K-Means algorithm.},
keywords = {Data mining,Clustering,K-means,Persistent K-Means},
url = {http://www.qjcr.ir/article_653.html},
eprint = {http://www.qjcr.ir/article_653_ea445cbd2ae85b13da50d22ab6244027.pdf}
}