Affiliations: [a] School of Mathematical Sciences, Dalian University of Technology, Dalian, China
| [b] Department of Mathematics, Jilin Institute of Chemical Technology, Jilin, China
Correspondence:
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Corresponding author. Yanli Shi, School of Mathematical Sciences, Dalian University of Technology, No. 2, Linggong Road, Ganjingzi District, Dalian City, Liaoning Province, 116024, PR China. Tel.: +86 15943283332; Fax: +86 41184707579; E-mail: [email protected].
Abstract: Fuzzy c-means is one of the most popular partitional clustering. However, it has the shortcoming that it is sensitive to initial centers and noises. Density-based clustering algorithm overcomes this shortcoming, but cannot obtain the better clustering results when the density of data space has uneven distribution. Grid-based method is advantageous to save computational time, but the clustering performance was unsatisfied. Based on the above analysis, the improved FCM algorithm based on initial center optimization method is proposed. First, the initial center optimization method based on density and grid is presented to avoid the sensitivity of FCM to initial centers. Then, improved FCM algorithm based on initial center optimization method is proposed. Finally, the performance and effectiveness of the proposed clustering algorithm is evaluated by 4 San Francisco taxi GPS cab mobility traces data sets, and the experimental results show that the proposed algorithm has better clustering results.
