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Fuzzy C-Mean Clustering Algorithm Modification and Adaptation for Applications

Author(s): Bassam M. El-Zaghmouri | Marwan A. Abu-Zanona

Journal: World of Computer Science and Information Technology Journal
ISSN 2221-0741

Volume: 2;
Issue: 1;
Start page: 42;
Date: 2012;
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Keywords: Fuzzy C-Mean | Clustering | Euclidean Distance.

Many clustering algorithms with different methodologies are subjected to be common techniques and main step in many applications in the computer science world. The need of adapting efficient clustering algorithm increases in critical applications (i.e. wireless sensors networks). Utilizing the Fuzzy Logic power; Fuzzy C-mean (FCM) clustering has a major role in most clustering applications. But in many cases, the result of FCM is considered to be non-complete clustering strategy. This paper adapted the FCM algorithm to enable of generating clusters with equal sizes. Also, scattered points that are located far away from all clusters are grouped out of clusters. Another modification is to localize specific points that have ability to locate in more than one cluster; hence this has a non-negligible importance in some fields such as cellular communications.
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