Affiliations: [a] School of Computer Science, Chengdu University of Information Technology, Chengdu, Sichuan 610225, China | [b] School of Information and Engineering, Sichuan Tourism University, Chengdu, Sichuan 610100, China | [c] School of Mathematics and Computer Science, Aba Teachers University, Wenchuan, Sichuan 623002, China
Correspondence:
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Corresponding author: Xiangbing Zhou, School of Information and Engineering, Sichuan Tourism University, Chengdu, Sichuan 610100, China. E-mail: zhouxb@uestc.edu.cn.
Abstract: GPS location data is rapidly increasing and has become an important part of spatial information technology and its applications. In order to use K-means to discover hidden information behind GPS data, the drawbacks of K-means must be addressed, such as the difficulty of discovering the number of clusters, sensitivity to initial cluster center (seed) selection, and ease of falling into local optima. This paper presents a novel sharing-based niche genetic algorithm (NGA) with a novel initial population approach based on hybrid K-means to obtain the best chromosome which is then used to perform K-means clustering (termed NicheClust). SSE, DB-index, PBM-index, and COSEC are used as fitness functions for NGA. The experimental results demonstrate that NicheClust has high performance and efficiency for three GPS location datasets.
