Topic discovery of clusters from documents with geographical location

doi:10.1002/cpe.3474

	Topic discovery of clusters from documents with geographical location
	Zhang, Li 1,4; Sun, Xiaoping 1,3; Hai Zhuge 1,2
	2015-10-01
发表期刊	CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
ISSN	1532-0626
卷号	27 期号:15 页码:4015-4038
摘要	As smart phones with GPS become popular, more and more textual documents with geographical locations are published on the Web. Keyword-based location services like vehicle navigation, tour planning, nearby object querying, and location pattern discovering of spatial objects are becoming popular and important. However, processing both text and geographical locations brings more challenges to information-retrieval techniques. In this paper, we focus on the problem of finding textual topics of clusters containing spatial objects with text descriptions. The key is how to combine clustering techniques with topic-retrieval models to integrate both geo-location information and text information. We investigated methods that combine clustering methods with the Latent Dirichlet Allocation model to discover topics of clusters of documents with geo-locations. Six different methods of combination are investigated, each having outputs with different meanings, which can be further leveraged to answer different types of queries over spatial documents. Experiments are conducted on both synthetic and real data. The results show that the combination of the probabilistic topic model with clustering algorithms is an efficient and effective way to discover meaningful clusters in different facets and levels of documents with textual and geographical information. Copyright (C) 2015 John Wiley & Sons, Ltd.
关键词	topic model clustering textual document geographical location
DOI	10.1002/cpe.3474
收录类别	SCI
语种	英语
资助项目	National Science Foundation of China[61075074] ; National Science Foundation of China[61070183] ; Open Fund Project of State Key Laboratory of Software Engineering (Wuhan University, China)[SKLSE2012-09-2]
WOS研究方向	Computer Science
WOS类目	Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:000363042100014
出版者	WILEY-BLACKWELL
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/9211
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Sun, Xiaoping
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Knowledge Grid Lab, Key Lab Intelligent Informat Proc, Beijing, Peoples R China 2.Nanjing Univ Posts & Telecommun, Nanjing, Jiangsu, Peoples R China 3.Wuhan Univ, State Key Lab Software Engn, Wuhan 430072, Hubei, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Li,Sun, Xiaoping,Hai Zhuge. Topic discovery of clusters from documents with geographical location[J]. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE,2015,27(15):4015-4038.
APA	Zhang, Li,Sun, Xiaoping,&Hai Zhuge.(2015).Topic discovery of clusters from documents with geographical location.CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE,27(15),4015-4038.
MLA	Zhang, Li,et al."Topic discovery of clusters from documents with geographical location".CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 27.15(2015):4015-4038.