An Optimized Approach for k-means Clustering
Sadhana Tiwari and Tanu Solanki. Article: An Optimized Approach for k-means Clustering. IJCA Proceedings on 9th International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness 2013 QShine:5-7, December 2013. Full text available. BibTeX
@article{key:article,
author = {Sadhana Tiwari and Tanu Solanki},
title = {Article: An Optimized Approach for k-means Clustering},
journal = {IJCA Proceedings on 9th International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness 2013},
year = {2013},
volume = {QShine},
pages = {5-7},
month = {December},
note = {Full text available}
}
Abstract
Cluster analysis method is one of the most analytical methods of data mining. The method will directly influence the result of clustering. This paper discusses the standard of k-mean clustering and analyzes the shortcomings of standard k-means such as k-means algorithm calculates distance of each data point from each cluster centre. Calculating this distance in each iteration makes the algorithm of low efficiency. This paper introduces an optimized algorithm which solves this problem. This is done by introducing a simple data structure to store some information in every iteration and used this information in next iteration. The introduced algorithm does not require calculating the distance of each data point from each cluster centre in each iteration due to which running time of algorithm is saved. Experimental results show that the improved algorithm can efficiently improve the speed of clustering and accuracy by reducing the computational complexity of standard k-means algorithm.
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