Abstract Soft subspace clustering algorithm frequently falls into local optimum during searching clustering center point. A fuzzy clustering algorithm is proposed based on the framework of soft subspace clustering, and it integrates quantum-behaved particle swarm optimization (QPSO) algorithm into gradient descent method to optimize the objective function in soft subspace clustering. By the characteristic of searching global optimum in the QPSO algorithm, global optimal center points are solved in the subspace, and then by the high convergence speed of the gradient descent method, fuzzy weights and membership degree matrices of sample points can be obtained. Finally, the optimal clustering results of sample points are obtained. Experiment is carried out on UCI dataset and the results demonstrate the improvement in accuracy as well as the stability of the clustering results of the proposed method.
Corresponding Authors:
WU Xiaojun (Corresponding author), born in 1967, Ph.D., professor. His research interests include artificial intelligence, pattern recognition and computer vision.
About author:: XU Yajun, born in 1990, master student. His research intere-sts include artificial intelligence and pattern recognition.
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2016, Vol. 29 
