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2013 (Vol. 5, Issue: 03)
Article Information:

Using GA for Optimization of the Fuzzy C-Means Clustering Algorithm

Mohanad Alata, Mohammad Molhim and Abdullah Ramini
Corresponding Author:  Mohanad Alata 

Key words:  Fuzzy c-means clustering, fuzzy inference system, genetic algorithm, model optimization, sugeno system, ,
Vol. 5 , (03): 695-701
Submitted Accepted Published
June 01, 2012 July 09, 2012 January 21, 2013
Abstract:

Fuzzy C-Means Clustering algorithm (FCM) is a method that is frequently used in pattern recognition. It has the advantage of giving good modeling results in many cases, although, it is not capable of specifying the number of clusters by itself. In FCM algorithm most researchers fix weighting exponent (m) to a conventional value of 2 which might not be the appropriate for all applications. Consequently, the main objective of this paper is to use the subtractive clustering algorithm to provide the optimal number of clusters needed by FCM algorithm by optimizing the parameters of the subtractive clustering algorithm by an iterative search approach and then to find an optimal weighting exponent (m) for the FCM algorithm. In order to get an optimal number of clusters, the iterative search approach is used to find the optimal single-output Sugeno-type Fuzzy Inference System (FIS) model by optimizing the parameters of the subtractive clustering algorithm that give minimum least square error between the actual data and the Sugeno fuzzy model. Once the number of clusters is optimized, then two approaches are proposed to optimize the weighting exponent (m) in the FCM algorithm, namely, the iterative search approach and the genetic algorithms. The above mentioned approach is tested on the generated data from the original function and optimal fuzzy models are obtained with minimum error between the real data and the obtained fuzzy models.
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  Cite this Reference:
Mohanad Alata, Mohammad Molhim and Abdullah Ramini, 2013. Using GA for Optimization of the Fuzzy C-Means Clustering Algorithm.  Research Journal of Applied Sciences, Engineering and Technology, 5(03): 695-701.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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