Mining fuzzy moving object clusters

Phan Nhat Hai, Dino Ienco, Pascal Poncelet, Maguelonne Teisseire

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Recent improvements in positioning technology have led to a much wider availability of massive moving object data. One of the objectives of spatio-temporal data mining is to analyze such datasets to exploit moving objects that travel together. Naturally, the moving objects in a cluster may actually diverge temporarily and congregate at certain timestamps. Thus, there are time gaps among moving object clusters. Existing approaches either put a strong constraint (i.e. no time gap) or completely relaxed (i.e. whatever the time gaps) in dealing with the gaps may result in the loss of interesting patterns or the extraction of huge amount of extraneous patterns. Thus it is difficult for analysts to understand the object movement behavior. Motivated by this issue, we propose the concept of fuzzy swarm which softens the time gap constraint. The goal of our paper is to find all non-redundant fuzzy swarms, namely fuzzy closed swarm. As a contribution, we propose fCS-Miner algorithm which enables us to efficiently extract all the fuzzy closed swarms. Conducted experiments on real and large synthetic datasets demonstrate the effectiveness, parameter sensitiveness and efficiency of our methods.

Original languageEnglish (US)
Title of host publicationAdvanced Data Mining and Applications - 8th International Conference, ADMA 2012, Proceedings
Pages100-114
Number of pages15
DOIs
StatePublished - 2012
Externally publishedYes
Event8th International Conference on Advanced Data Mining and Applications, ADMA 2012 - Nanjing, China
Duration: Dec 15 2012Dec 18 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7713 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Advanced Data Mining and Applications, ADMA 2012
CountryChina
CityNanjing
Period12/15/1212/18/12

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Frequent itemset
  • Fuzzy closed swarm
  • Fuzzy time gap

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