Self-Defined Fuzzy Clustering C-Means Algorithm Based on Improved Bat Optimization
TANG Zheng-hua
Information Technology Department, Shandong Provincial Party School of the CPC, Jinan, Shandong 250014,China
Abstract For the fuzzy clustering C-means algorithm is sensitive to the initial clustering center and clustering slow convergence, manually set the number of clusters and other defects, self-defined fuzzy clustering C-means algorithm based on improved bat optimization was proposed.Based on the density peak value, the density of data and the distance between cluster centers were measured, so as to automatically determine the number of cluster centers and clusters, which was used as the initial center of the improved bat algorithm.The Levy flight characteristics were introduced to enhance the bat algorithm to jump out of the local optimum ability, and Powell local search was used to accelerate bat algorithm convergence.The improved bat population was used for population optimization, and the optimal bat position was used as the clustering C-means new clustering center, and fuzzy clustering was carried out to obtain the clustering results by repeated iterative iterations.Compared with the other two clustering algorithms on the standard dataset, the experimental results showed that the proposed clustering algorithm can converge quickly with lower error rate.
Key words ：
metrology
fuzzy C-mean clustering
bat algorithm
levy flight
powell Local Search
density peak
automatic determination
Received: 13 June 2018
1 宋旭东, 朱文辉, 邱占芝. 大数据k-Means聚类挖掘优化算法[J].大连交通大学学报, 2015, 36(3): 91-94. SongX D , ZhuW H, QiuZ Z. Big Data K-Means Clustering Mining Optimization Algorithm[J].Journal of Dalian Jiaotong University, 2015, 36(3): 91-94.
2020, Vol. 41 
