A Synthesized Approach for Comparison and Enhancement of Clustering Algorithms in Data Mining for Improving Feature Quality
Heena Sharma1, Navdeep Kaur Kaler2
1Heena Sharma, Research Scholar, Done B.Tech. (CSE), L.L.R.I.E.T Moga (P.T.U), Punjab, India.
2Navdeep Kaur Kaler, Assistant Professor in Department Of (CSE), L.L.R.I.E.T, Moga, Punjab, India.
Manuscript received on April 28, 2014. | Revised Manuscript received on May 03, 2014. | Manuscript published on May 05, 2014. | PP: 114-117 | Volume-4 Issue-2, May 2014. | Retrieval Number: B2226054214/2014©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: K-Means and Kohonen SOM clustering are two major analytical tools for unsupervised forest datasets. However, both have their innate disadvantages. Clustering is currently one of the most crucial techniques for dealing with massive amount of heterogeneous information on the databases, which is beyond human being’s capacity to digest. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. Also, as clusters grow in size, the actual expression patterns become less relevant. K-means clustering requires a specified number of clusters in advance and chooses initial centroids randomly; in addition, it is sensitive to outliers. SOM We present an improved approach to combined merits of the two and discard disadvantages.
Keywords: Clustering, K-means, Kohonen SOM, Data Mining.