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A New Scalability of Hybrid Fuzzy C-Means Algorithm

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3 Author(s)
Hao Wang ; Sch. of Comput. & Inf., Fuyang Teachers Coll., Fuyang, China ; Danyun Li ; Yayun Chu

In this paper, a new scalability of hybrid fuzzy clustering algorithm that incorporates the Fuzzy C-means into the Quantum-behaved Particle Swarm Optimization algorithm is proposed. The QPSO has less parameters and higher convergent capability of the global optimizing than Particle Swarm Optimization algorithm. So the iteration algorithm is replaced by the new hybrid algorithm based on the gradient descent of FCM, which makes the algorithm have a strong global searching capacity and avoids the local minimum problems of FCM and avoids depending on the initialization values. The simulation result proves that compared with other algorithms, the new algorithm not only has the favorable convergence capability of the global optimizing but also has been obviously improved the clustering effect.

Published in:

Artificial Intelligence and Computational Intelligence (AICI), 2010 International Conference on  (Volume:3 )

Date of Conference:

23-24 Oct. 2010