Optimized PSO-FCM Cluster Algorithm Based on Principal Component Analysis
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    Abstract:

    For multi-cluster problems, PSO-FCM cluster algorithm is lack of performance and easily leads to local optimum, which affects the accuracy of multi-cluster result. To tackle these issues, an optimized PSO-FCM cluster algorithm based on PCA is put forward. By introducing PCA processing method, setting different movement weight on each dimension of particle and reducing particle sensitivity, reasonably controlling movement speed of particles on each dimension and effectively decreasing unconstrained particles on each dimension, possibility of moving into false cluster is increased due to over-sensitive particles on interface of multi-cluster groups. This paper introduces related conditions of PSO-FCM algorithm briefly and the proposed optimized algorithm in detail. Finally, this paper presents the experiment results, i.e., the optimized algorithm proposed in this study is totally better than other algorithms in many data sets.

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陈诚,刘振宇.基于PCA优化的PSO-FCM聚类算法.计算机系统应用,2020,29(3):213-217

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History
  • Received:July 29,2019
  • Revised:September 05,2019
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  • Online: March 02,2020
  • Published: March 15,2020
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