A Review of Image Data Clustering Techniques
Ashwini Gulhane1, Prashant L. Paikrao2, D. S. Chaudhari3

1PAshwini U. Gulhane, Eectronics and Telecommunication Dept. Amravati University, GCOE Amravati, India.
2Prashant L. Paikrao, B.E. degree in Industrial Electronics from Dr. BAM University, Aurangabad and M. Tech. degree in Electronics from SGGSIE&T Nanded, India.
3Devendra S. Chaudhari, Electronics and Telecommunication Dept, BE, ME, from Marathwada University, Aurangabad and PhD from Indian Institute of Technology, Bombay, Mumbai, India.

Manuscript received on February 15, 2012. | Revised Manuscript received on February 29, 2012. | Manuscript published on March 05, 2012. | PP: 212-215 | Volume-2 Issue-1, March 2012. | Retrieval Number: A0417022112 /2012©BEIESP
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© The Authors. Published By: 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: In order to the find the close association between the density of data points, in the given data set of pixels of an image, clustering provides an easy analysis and proper validation. In this paper various clustering techniques along with some clustering algorithms are described. Further k-means algorithm, its limitations and a new approach of clustering called as M-step clustering that may overcomes these limitations of k-means is included.

Keywords: M-step clustering, k-means clustering.