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 journalArticle

11 Citations (Scopus)

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.

Original languageEnglish
Article number6731551
Pages (from-to)2717-2730
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume26
Issue number11
DOIs
Publication statusPublished - 2014 Jan 1

Fingerprint

Trajectories
Location based services
Data structures
Experiments

Keywords

  • and trajectory pattern mining
  • community structure
  • Trajectory profile

ASJC Scopus subject areas

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

Cite this

Exploring sequential probability tree for movement-based community discovery. / Zhu, Wen Yuan; Peng, Wen Chih; Hung, Chih Chieh; Lei, Po Ruey; Chen, Ling Jyh.

In: IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No. 11, 6731551, 01.01.2014, p. 2717-2730.

Research output: Contribution to journalArticle

Zhu, Wen Yuan ; Peng, Wen Chih ; Hung, Chih Chieh ; Lei, Po Ruey ; Chen, Ling Jyh. / Exploring sequential probability tree for movement-based community discovery. In: IEEE Transactions on Knowledge and Data Engineering. 2014 ; Vol. 26, No. 11. pp. 2717-2730.
@article{0618c0d0124542f49152e9d852934e3b,
title = "Exploring sequential probability tree for movement-based community discovery",
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.",
keywords = "and trajectory pattern mining, community structure, Trajectory profile",
author = "Zhu, {Wen Yuan} and Peng, {Wen Chih} and Hung, {Chih Chieh} and Lei, {Po Ruey} and Chen, {Ling Jyh}",
year = "2014",
month = "1",
day = "1",
doi = "10.1109/TKDE.2014.2304458",
language = "English",
volume = "26",
pages = "2717--2730",
journal = "IEEE Transactions on Knowledge and Data Engineering",
issn = "1041-4347",
publisher = "IEEE Computer Society",
number = "11",

}

TY - JOUR

T1 - Exploring sequential probability tree for movement-based community discovery

AU - Zhu, Wen Yuan

AU - Peng, Wen Chih

AU - Hung, Chih Chieh

AU - Lei, Po Ruey

AU - Chen, Ling Jyh

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

AB - 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.

KW - and trajectory pattern mining

KW - community structure

KW - Trajectory profile

UR - http://www.scopus.com/inward/record.url?scp=84923163927&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84923163927&partnerID=8YFLogxK

U2 - 10.1109/TKDE.2014.2304458

DO - 10.1109/TKDE.2014.2304458

M3 - Article

AN - SCOPUS:84923163927

VL - 26

SP - 2717

EP - 2730

JO - IEEE Transactions on Knowledge and Data Engineering

JF - IEEE Transactions on Knowledge and Data Engineering

SN - 1041-4347

IS - 11

M1 - 6731551

ER -