A fast space-saving algorithm for maximal co-location pattern mining

	A fast space-saving algorithm for maximal co-location pattern mining
	Yao, Xiaojing 1; Peng, Ling 1; Yang, Liang 1; Chi, Tianhe 1
刊名	Expert Systems with Applications
	2016
卷号	63 页码:310-323
关键词	UPPER TROPOSPHERIC METHANE CH4 VALIDATION IASI CO2 RESOLUTION SOUNDER SPACE
通讯作者	Peng, Ling (pengling@radi.ac.cn)
英文摘要	Real space teems with potential feature patterns with instances that frequently appear in the same locations. As a member of the data-mining family, co-location can effectively find such feature patterns in space. However, given the constant expansion of data, efficiency and storage problems become difficult issues to address. Here, we propose a maximal-framework algorithm based on two improved strategies. First, we adopt a degeneracy-based maximal clique mining method to yield candidate maximal co-locations to achieve high-speed performance. Motivated by graph theory with parameterized complexity, we regard the prevalent size-2 co-locations as a sparse undirected graph and subsequently find all maximal cliques in this graph. Second, we introduce a hierarchical verification approach to construct a condensed instance tree for storing large instance cliques. This strategy further reduces computing and storage complexities. We use both synthetic and real facility data to compare the computational time and storage requirements of our algorithm with those of two other competitive maximal algorithms: “order-clique-based” and “MAXColoc”. The results show that our algorithm is both more efficient and requires less storage space than the other two algorithms. © 2016 Elsevier Ltd
学科主题	Computer Science; Engineering; Operations Research & Management Science
类目[WOS]	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:20162902610014
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39248]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1. Lab of Spatial Information Integration, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 20 North, Datun Road, Chaoyang District, Beijing 2.100101, China 3. School of Information Engineering, Tianjin University of Commerce, No. 409, Guangrong Road, Beichen District, Tianjin 4.300134, China
推荐引用方式 GB/T 7714	Yao, Xiaojing,Peng, Ling,Yang, Liang,et al. A fast space-saving algorithm for maximal co-location pattern mining[J]. Expert Systems with Applications,2016,63:310-323.
APA	Yao, Xiaojing,Peng, Ling,Yang, Liang,&Chi, Tianhe.(2016).A fast space-saving algorithm for maximal co-location pattern mining.Expert Systems with Applications,63,310-323.
MLA	Yao, Xiaojing,et al."A fast space-saving algorithm for maximal co-location pattern mining".Expert Systems with Applications 63(2016):310-323.