kEFCM: kNN-Based Dynamic Evolving Fuzzy Clustering Method
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2015, Vol 6, Issue 2
Abstract
Despite the recent emergence of research, creating an evolving fuzzy clustering method that intelligently copes with huge amount of data streams in the present high-speed networks involves a lot of difficulties. Several efforts have been devoted to enhance traditional clustering techniques into on-line evolving fuzzy able to learn and develop continuously. In line with these efforts, we propose kEFCM, kNN-based evolving fuzzy clustering method. kEFCM overcomes the problems of computational cost, dynamic fuzzy evolving, and clustering complexity of traditional kNN. It employs the least-squares method in determining the cluster center and influential area, as well as the Euclidean distance in identifying the membership degree. It enhances the traditional kNN algorithm by involving only cluster centers in making classification decisions and evolving on-line the clusters when a new data arrives. For evaluation purpose, the experimental results on a collection of benchmark datasets are compared against other well-known clustering methods. The evaluation results approve a good competitive level of kEFCM.
Authors and Affiliations
Shubair Abdulla, Amer Al-Nassiri
Keywords
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- EP ID EP147961
- DOI 10.14569/IJACSA.2015.060202
- Views 87
- Downloads 0
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