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dc.contributor.authorZhu, Wen-Yuanen_US
dc.contributor.authorPeng, Wen-Chihen_US
dc.contributor.authorHung, Chih-Chiehen_US
dc.contributor.authorLei, Po-Rueyen_US
dc.contributor.authorChen, Ling-Jyhen_US
dc.date.accessioned2014-12-08T15:36:55Z-
dc.date.available2014-12-08T15:36:55Z-
dc.date.issued2014-11-01en_US
dc.identifier.issn1041-4347en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TKDE.2014.2304458en_US
dc.identifier.urihttp://hdl.handle.net/11536/25319-
dc.description.abstractIn this paper, we tackle the problem of discovering movement-based communities of users, where users in the same community have similar movement behaviors. Note that the identification of movement-based communities is beneficial to location-based services and trajectory recommendation services. Specifically, we propose a framework to mine movement-based communities which consists of three phases: 1) constructing trajectory profiles of users, 2) deriving similarity between trajectory profiles, and 3) discovering movement-based communities. In the first phase, we design a data structure, called the Sequential Probability tree (SP-tree), as a user trajectory profile. SP-trees not only derive sequential patterns, but also indicate transition probabilities of movements. Moreover, we propose two algorithms: BF (standing for breadth-first) and DF (standing for depth-first) to construct SP-tree structures as user profiles. To measure the similarity values among users\' trajectory profiles, we further develop a similarity function that takes SP-tree information into account. In light of the similarity values derived, we formulate an objective function to evaluate the quality of communities. According to the objective function derived, we propose a greedy algorithm Geo-Cluster to effectively derive communities. To evaluate our proposed algorithms, we have conducted comprehensive experiments on two real data sets. The experimental results show that our proposed framework can effectively discover movement-based user communities.en_US
dc.language.isoen_USen_US
dc.titleExploring Sequential Probability Tree for Movement-Based Community Discoveryen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TKDE.2014.2304458en_US
dc.identifier.journalIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERINGen_US
dc.citation.volume26en_US
dc.citation.issue11en_US
dc.citation.spage2717en_US
dc.citation.epage2730en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000343607500010-
dc.citation.woscount0-
Appears in Collections:Articles