A Parallel Algorithm for Mining Time Relaxed Gradual Clustering Pattern Based on Spatio-Temporal Trajectories

Hongyan Sun, Genlin Ji, Bin Zhao, Xintao Liu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

As an important area of spatio-Temporal data mining, time relaxed gradual clustering pattern has been attracting broad attention in recent years. Algorithm PTRGSP is proposed to mining time relaxed gradual clustering pattern from spatio-Temporal trajectory, which is implemented under spark framework. All trajectories are divided into point sets with different timestamps, and such point sets are parallel clustered. After that, clusters of different time intervals are joined in parallel to achieve pattern candidates. Candidates are combined to obtain interesting maximal time relaxed gradual clustering pattern. To improve the efficiency of the algorithm PTRGSP, algorithm PTRGSP-G is presented based on grid index for mining time relaxed gradual clustering pattern. The experiment results on real dataset and synthetic trajectory dataset demonstrate the effectiveness and efficiency of the two algorithms.

Original languageEnglish
Title of host publicationProceedings - 5th International Conference on Advanced Cloud and Big Data, CBD 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages308-313
Number of pages6
ISBN (Electronic)9781538610725
DOIs
Publication statusPublished - 6 Sep 2017
Event5th International Conference on Advanced Cloud and Big Data, CBD 2017 - Shanghai, China
Duration: 13 Aug 201716 Aug 2017

Publication series

NameProceedings - 5th International Conference on Advanced Cloud and Big Data, CBD 2017

Conference

Conference5th International Conference on Advanced Cloud and Big Data, CBD 2017
Country/TerritoryChina
CityShanghai
Period13/08/1716/08/17

Keywords

  • Parallel data mining
  • Spatio-Temporal trajectory
  • Time relaxed gradual clustering pattern

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management

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