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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/106217

    Title: Exploring Sequential Probability Tree for Movement-based Community Discovery
    Authors: Zhu, Wen-Yuan;Peng, Wen-Chih;Hung, Chih-Chieh;Lei, Po-Ruey;Chen, Ling-Jyh
    Keywords: Trajectory profile;community structure;trajectory pattern mining
    Date: 2014-11-01
    Issue Date: 2016-04-22 13:42:08 (UTC+8)
    Publisher: Institute of Electrical and Electronics Engineers
    Abstract: In 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.
    DOI: 10.1109/TKDE.2014.2304458
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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