DocumentCode :
3392399
Title :
A New Scalability of Hybrid Fuzzy C-Means Algorithm
Author :
Wang, Hao ; Li, Danyun ; Chu, Yayun
Author_Institution :
Sch. of Comput. & Inf., Fuyang Teachers Coll., Fuyang, China
Volume :
3
fYear :
2010
fDate :
23-24 Oct. 2010
Firstpage :
55
Lastpage :
58
Abstract :
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.
Keywords :
fuzzy set theory; particle swarm optimisation; pattern classification; pattern clustering; search problems; statistical analysis; fuzzy clustering algorithm; global searching capacity; gradient descent; higher convergent capability; hybrid fuzzy C-means algorithm; iteration algorithm; local minimum problems; quantum-behaved particle swarm optimization; scalability; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Convergence; Databases; Equations; Particle swarm optimization; FCM; Fuzzy Clustering; Particle Swarm Optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Artificial Intelligence and Computational Intelligence (AICI), 2010 International Conference on
Conference_Location :
Sanya
Print_ISBN :
978-1-4244-8432-4
Type :
conf
DOI :
10.1109/AICI.2010.252
Filename :
5655161
Link To Document :
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