文章基本信息

标题：Station Segmentation with an Improved K-Means Algorithm for Hangzhou Public Bicycle System
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作者：Xu, Haitao ; Ying, Jing ; Lin, Fei 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2013
卷号：8
期号：9
页码：2289-2296
DOI：10.4304/jsw.8.9.2289-2296
语种：English
出版社：Academy Publisher
摘要：In China, Hangzhou is the first city to establish the Public Bicycle System. Now, the system has been the largest bike- sharing program in the world. The software of Hangzhou Public Bicycle System was developed by our team. There are many and many technology problems in the decision of intelligent dispatch. Among of these problems, determining how to segment the stations into several sections to give different care is very important. In this paper, an improved k-means algorithm based on optimized simulated annealing is used to segment the stations of Hangzhou Public Bicycle System. The optimized simulated annealing(SA) algorithm is used to assign k-means initial cluster centers. Practice examples and comparison with the traditional k-means algorithm are made. The results show that the proposed algorithm is efficient and robust. The research result has been applied in Hangzhou.
关键词：Public Bicycle System;K-means algorithm;data mining;Segmentation