Rough Fuzzy K-means Clustering Algorithm Based on Mixed Metrics and Cluster Adaptive Adjustment
ZHANG Xintao1, MA Fumin1, CAO Jie1, ZHANG Tengfei2
1.College of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023; 2.College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003
Abstract：Rough K-means clustering and its related derivative algorithms need the number of clusters in advance, and random selection of the initial cluster center results in low accuracy of data partition in the cross-region of clusters. To solve these problems, a rough fuzzy K-means clustering algorithm with adaptive adjustment of clusters is proposed. When the membership degree of the data objects belonging to different clusters in the intersection area of the cluster boundary is calculated, the mixed metrics of local density and distance are taken into account in the proposed algorithm. The optimal number of clusters is gained by adjusting the number of clusters adaptively. The midpoint of two samples with the smallest distance in the dense area of data objects is selected as the initial cluster center. The object with the local density higher than the average density is divided into the cluster, and then the re-maining initial cluster center can be selected. Thus, the selection of the initial cluster centers is more reasonable. The experiments on synthetic datasets and UCI datasets demonstrate the advantages of the proposed algorithm in adaptability and clustering accuracy for dealing with spherical clusters with blurred boundaries.
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2019, Vol. 32 
