Exploring sequential probability tree for movement-based community discovery

Wen Yuan Zhu, Wen Chih Peng, Chih Chieh Hung, Po Ruey Lei, Ling Jyh Chen

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


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.

Original languageEnglish
Article number6731551
Pages (from-to)2717-2730
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number11
Publication statusPublished - 2014 Nov
Externally publishedYes


  • Trajectory profile
  • and trajectory pattern mining
  • community structure

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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