基于SimRank的公共自行车站点聚类算法  

Public Bike Station Clustering Algorithm Based on SimRank

作　　者：朱金山[1] 刘良旭[2] 周超兰 管博[2] ZHU Jinshan;LIU Liangxu;ZHOU Chaolan;GUAN Bo(Library and Information Center,Ningbo Institute of Technology,Zhejiang University,Ningbo,Zhejiang 315100,China;School of Electronic and Information Engineering,Ningbo University of Technology,Ningbo,Zhejiang 315211,China)

机构地区：[1]浙江大学宁波理工学院图书信息中心,浙江宁波315100 [2]宁波工程学院电子与信息工程学院,浙江宁波315211

出　　处：《计算机工程》2018年第4期12-16,共5页Computer Engineering

基　　金：浙江省自然科学基金(LY14F020007);浙江省公益技术应用研究计划项目(2016C33255);宁波市自然科学基金(2014A610072)

摘　　要：针对城市公共自行车系统快速发展面临的潮汐问题,提出一种基于SimRank的自行车站点聚类算法。从站点间的关联关系出发,基于站点特性定义站点相似度,引入SimRank算法进行站点相似度计算,并按计算得到的相似度值,根据最大相似优先的思想对站点进行聚类。实验结果表明,该算法得到的聚类结果能准确反映自行车流趋势和区域特征,其中同聚类成员也具有较大的关联性。In view of the tide problem of fast developed city public bike system,this paper proposes a station clustering algorithm based on SimRank,which uses the characteristics of public bike.Firstly,the definition of station similarity is proposed based on the relation between stations.Secondly,the SimRank algorithm is introduced to calculate the similarity between stations.Finally,according to the calculated similarity values,the stations are clustered with the idea of maximum similarity priority.Experimental results show that the clustering results by the proposed algorithm have accurate bike flow characteristic and regional characteristic,m eanw hile,the members in same cluster have great relevance.

关 键 词：公共自行车 聚类分析 站点联系 站点特征 相似度矩阵 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]