TY - JOUR
ID - 653
TI - Persistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm
JO - Journal of Computer & Robotics
JA - JCR
LA - en
SN - 2345-6582
AU - Azimi, Rasool
AU - Sajedi, Hedieh
AD - Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
AD - Department of Computer Science, College of Science, University of Tehran, Tehran, Iran
Y1 - 2014
PY - 2014
VL - 7
IS - 1
SP - 57
EP - 66
KW - Data mining
KW - Clustering
KW - K-means
KW - Persistent K-Means
DO -
N2 - 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.
UR - http://www.qjcr.ir/article_653.html
L1 - http://www.qjcr.ir/article_653_ea445cbd2ae85b13da50d22ab6244027.pdf
ER -