On discovering moving clusters in spatio-temporal data

Panos Kalnis, Nikos Mamoulis, Spiridon Bakiras

Research output: Contribution to journalArticle

226 Citations (Scopus)

Abstract

A moving cluster is defined by a set of objects that move close to each other for a long time interval. Real-life examples are a group of migrating animals, a convoy of cars moving in a city, etc. We study the discovery of moving clusters in a database of object trajectories. The difference of this problem compared to clustering trajectories and mining movement patterns is that the identity of a moving cluster remains unchanged while its location and content may change over time. For example, while a group of animals are migrating, some animals may leave the group or new animals may enter it. We provide a formal definition for moving clusters and describe three algorithms for their automatic discovery: (i) a straight-forward method based on the definition, (ii) a more efficient method which avoids redundant checks and (iii) an approximate algorithm which trades accuracy for speed by borrowing ideas from the MPEG-2 video encoding. The experimental results demonstrate the efficiency of our techniques and their applicability to large spatio-temporal datasets.

Original languageEnglish
Pages (from-to)364-381
Number of pages18
JournalLecture Notes in Computer Science
Volume3633
Publication statusPublished - 2005
Externally publishedYes

Fingerprint

Spatio-temporal Data
Animals
Trajectories
Trajectory
MPEG-2
Approximate Algorithm
Straight
Mining
Encoding
Railroad cars
Clustering
Interval
Experimental Results
Demonstrate
Object

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

On discovering moving clusters in spatio-temporal data. / Kalnis, Panos; Mamoulis, Nikos; Bakiras, Spiridon.

In: Lecture Notes in Computer Science, Vol. 3633, 2005, p. 364-381.

Research output: Contribution to journalArticle

Kalnis, Panos ; Mamoulis, Nikos ; Bakiras, Spiridon. / On discovering moving clusters in spatio-temporal data. In: Lecture Notes in Computer Science. 2005 ; Vol. 3633. pp. 364-381.
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