The proposal of a fuzzy clustering algorithm based on particle swarm

2011 ◽  
Author(s):  
Alexandre Szabo ◽  
Leandro Nunes de Castro ◽  
Myriam Regattieri Delgado
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Fuzzy Clustering Algorithm

scholarly journals Service Partition Method Based on Particle Swarm Fuzzy Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hong Xia ◽  
Qingyi Dong ◽  
Hui Gao ◽  
Yanping Chen ◽  
ZhongMin Wang
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Particle Swarm ◽  
Cluster Center ◽  
Fuzzy Clustering Algorithm ◽  
Partition Method ◽  
Service Data ◽  
Optimal Cluster ◽  
Better Than

It is difficult to accurately classify a service into specific service clusters for the multirelationships between services. To solve this problem, this paper proposes a service partition method based on particle swarm fuzzy clustering, which can effectively consider multirelationships between services by using a fuzzy clustering algorithm. Firstly, the algorithm for automatically determining the number of clusters is to determine the number of service clusters based on the density of the service core point. Secondly, the fuzzy c -means combined with particle swarm optimization algorithm to find the optimal cluster center of the service. Finally, the fuzzy clustering algorithm uses the improved Gram-cosine similarity to obtain the final results. Extensive experiments on real web service data show that our method is better than mainstream clustering algorithms in accuracy.

Fuzzy Clustering Algorithm Based on Improved Particle Swarm Optimization and its Application

2010 ◽  
Vol 44-47 ◽  
pp. 4067-4071 ◽  
Author(s):  
Xue Yong Li ◽  
Jia Xia Sun ◽  
Jun Hui Fu ◽  
Guo Hong Gao
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Dimensional Space ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Solution Space ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy Clustering Algorithm

A fuzzy clustering algorithm based on improved particle swarm optimization was proposed in this paper. First reduce dimension of solution space, separate it into smaller solution space. In separated solution space, use of improved particle swarm optimization algorithm to search the sub-optimal solution as a chromosome of whole particle,use improved PSO to search global optimal solution. The particle solve the problem that swarm algorithm easy to fall into local optimal solution in high dimensional space, and the problem that the fuzzy clustering algorithm is sensitive to initial value problems. Simulation results show the effectiveness of this algorithm.

Fault Diagnosis Based on Particle Swarm Fuzzy Clustering Algorithm

2011 ◽  
Vol 63-64 ◽  
pp. 111-114
Author(s):  
Zhi Xiao Wang ◽  
Qiang Niu
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Data Set ◽  
Fuzzy Clustering Algorithm ◽  
Efficient Performance ◽  
New Strategy ◽  
Fuzzy C Means Clustering ◽  
Search Capability

Fuzzy c-means clustering algorithm (FCM) is sensitive to noise and less effective when handling high dimensional data set. Given that particle swarm optimization algorithm (PSO) has strong global search capability and efficient performance, a new PSO based fuzzy clustering algorithm is proposed. Particles in the new algorithm are encoded by membership in FCM. The new algorithm adopts a new strategy to meet the constraints of FCM, so as to optimize the clustering effect of FCM. Finally, this algorithm is applied to motor fault diagnosis. Experiment shows that the new algorithm made up for the shortcomings of FCM, improved the efficiency and accuracy of clustering and bettered fault diagnosis results.

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