Exploring sequential probability tree for movement-based community discovery

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

研究成果: 雜誌貢獻期刊論文同行評審

12 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
文章編號6731551
頁(從 - 到)2717-2730
頁數14
期刊IEEE Transactions on Knowledge and Data Engineering
26
發行號11
DOIs
出版狀態已發佈 - 2014 十一月

ASJC Scopus subject areas

  • 資訊系統
  • 電腦科學應用
  • 計算機理論與數學

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