MSOM: Modified Self Organizing Map for Faster Winning Cluster Detection

R. Muhssen, Atheer; T. AL-obaidy, Abeer

	MSOM: Modified Self Organizing Map for Faster Winning Cluster Detection
Iraqi Journal of Information Technology
Article 1, Volume 7, Issue 4, 2017, Pages 144-163
Authors
Atheer R. Muhssen; Abeer T. AL-obaidy
Abstract
There are large number of modern techniques in today’s world which is evolving for collecting big data at different databases. Organized information investigation techniques are important to pick up/concentrate valuable data from quickly developing datasets. K-means Clustering investigation strategy is one of the generally utilized expository strategies as a part of numerous information mining applications. This paper including two stages in dealing with large and big datasets, the first stage discusses two clustering algorithms Self Organizing Map (SOM) and k-means about Performance Evaluation of K-Means and SOM Clustering. Using (C# and Matlab) programming language and the performance for k-means and SOM clustering algorithm is calculate based on the accuracy and running time. The second stage proposed modified self-organizing map(MSOM)to select winner cluster and extract this cluster from rapidly growing datasets which contain a hundred or more clusters in a very little time. This modification will appeared in two places in SOM standard algorithm. Evaluation results and conclusion will discuss in the last section. The purpose of the proposed Modified Self Organizing Map (MSOM) is to facilitate the search and access to the class that contains the desired product in the case of high number of classes where the proposed method to finding the best Class containing the desired product.
Keywords
Self organizing maps; means; clustering algorithm; datasets

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