A Study of Various Methods to find K for K-Means Clustering
Keywords:
K-Means, Clustering, Centroid, Centroid based clustering, partition based clustering, point based, convex, euclidianAbstract
Clustering is the technique which used to group data from a set of unlabeled data, in a way that data containing similar properties contains in a same group. There are many cluster techniques are used to cluster data thus there is no suitable definition for cluster is available. Techniques like link based clustering, centroid based clustering, distribution based clustering, density based clustering are used. A survey over centroid based K-mean clustering techniques is presented which is widely used for clustering purpose. K-mean clustering technique suffers drawbacks like sensitive to initialization centroid, sensitive to noise, and there is no. of clusters also not defined. Thus an enhanced k-mean technique is presented to reduce such drawbacks and provide an enhanced functionality for clustering.
References
Madhu Yedla, Srinivasa Rao Pathakota, and T M Srinivasa, "Enhancing K-means Clustering Algorithm with Improved Initial Center," in IJCSIT, 2010.
Boris Mirkin Mark Ming-Tso Chiang, "Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads," journal of classification, 2009.
S S Dimov, and C D Nguyen D T Pham, "Selection of K in K-means clustering ," IMechE 2005.
Naveen D Chandavarkar Uday Kumar S, "A Survey on Several Technical Methods for Selecting Initial Cluster Centers in K-Means Clustering Algorithm," IJARCSSE, Dec 2014.
David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, Angela Y. Wu Tapas Kanungo, "An Efficient k-Means Clustering Algorithm: Analysis and Implementation," IEEE.
Rebecca J. Passonneau, Austin Lee, Axinia Radeva, Boyi Xie And David Waltz Haimonti Dutta, "Learning Parameters Of The K-Means Algorithm, From Subjective Human Annotation," in Association for the Advancement of Artificial Intelligence, 2011.
H.J. Mucha, "Adaptive cluster analysis, classification and multivariate graphics," Weirstrass Institute for Applied Analysis and Stochastics, 1992.
N.V. Anand Kumar and G. V. Uma, "Improving Academic Performance of Students by Applying Data Mining Technique," European Journal of Scientific Research, vol. 34, 2009.
Navjot Kaur Manjot Kaur, "Web Document Clustering Approaches Using K-Means Algorithm," IJARCSSE, 2013.
Marian Cristian Mihaescu, Mihai Mocanu Cosmin Marian Poteras, "An Optimized Version of the K-Means Clustering Algorithm," in IEEE, 2014.
O.O. Oladipupo, I.C Obagbuwa O.J. Oyelade, "Application of k-Means Clustering algorithm for prediction of Students’ Academic Performance," IJCSIS, 2010.
A. Jamshidzadeh , M. Saadatseresht , S. Homayouni A. Alizade Naeini, "An Efficient Initialization Method For K-Means Clustering Of Hyper spectral Data," ISPRS, Nov 2014.
Zhiyi Fang Chunfei Zhang, "An Improved K-means Clustering Algorithm," in JICS, 2013.
Wenbin, Yang,Yan &Qu Wu, "Interactive visual summary of major communities in a large network," in Pacific Visualization Symposium, Hangzhou,China, 2015, pp. 47-54.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
