Experimental Study of an Improvedk-Means Algorithm and Its Comparison with Standardk-Means
Sonal Miglani1, KanwalGarg2
1Sonal Miglani, Research Scholar, M.Tech.(CSE) Dept. of Computer Science & Applications Kurukshetra University, Kurukshetra, India.
2Kanwal Garg, Assistant Professor Dept. of Computer Science & Applications Kurukshetra University, Kurukshetra, India.
Manuscript received on May 28, 2013. | Revised Manuscript received on June 17, 2013. | Manuscript published on June 30, 2013. | PP: 252-254 | Volume-2, Issue-5, June 2013. | Retrieval Number: E1780062513/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: K-means algorithm is a popular, unsupervised and iterative clustering algorithm well known for its efficiency in clustering large datasets. It is used in a variety of scientific applications such as knowledge discovery, Data Mining, data compression, medical imaging and vector quantization. This paper aims at studying the standard k-means clustering algorithm, analyzing its shortcomings and its comparison with an improved k-means algorithm. Experimental results show that the improved method can effectively increase the speed of clustering and accuracy, reducing the computational complexity of the kmeans.
Keywords: Clustering, Data Mining, K-Means Clustering.