标题:Learning Similarity Functions for Urban Events Detection by Mining Hotline Phone Records
作者:Ren, Pengjie; Liu, Peng; Chen, Zhumin; Ma, Jun; Song, Xiaomeng
作者机构:[Ren, Pengjie; Liu, Peng; Chen, Zhumin; Ma, Jun; Song, Xiaomeng] Shandong Univ, Dept Comp Sci & Technol, Jinan 250101, Peoples R China.
会议名称:17th Asia-Pacific Web Conference (APWeb)
会议日期:SEP 18-20, 2015
来源:WEB TECHNOLOGIES AND APPLICATIONS (APWEB 2015)
出版年:2015
卷:9313
页码:411-423
DOI:10.1007/978-3-319-25255-1_34
关键词:Event Detection; Data Mining; Urban Computing; Machine Learning
摘要:Many cities around the world have established a platform, entitled public service hotline, to allow citizens to tell about city issues, e.g. noise nuisance, or personal encountered problems, e.g. traffic accident, by making a phone call. As a result of "crowd sensing", these records contain rich human intelligence that can help to detect urban events. In this paper, we present an event detection approach to detect urban events based on phone records. Specifically, given a set of phone records in a period of time, we first learn a similarity matrix. Each element of the matrix is estimated as the probability that the corresponding pair of records describe the same event. Then, we propose an Improved Affinity Propagation (IAP) clustering approach which takes the similarity matrix as input and generates clusters as output. Each cluster is an urban event composed of several records. Extensive experiments demonstrate the great improvement of IAP on three standard datasets for clustering and the effectiveness of our event detection approach on real data from a hotline.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84950276371&doi=10.1007%2f978-3-319-25255-1_34&partnerID=40&md5=d343a3050a9a6f35534699633a9c7725
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