Direction clustering for characterizing movement patterns

Wenjun Zhou, Hui Xiong, Yong Ge, Jannite Yu, Hasan Ozdemir, K. C. Lee

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

3 Scopus citations

Abstract

The increasing availability of motion data creates unprecedent opportunities to change the paradigm for characterizing movement patterns. While cluster analysis is usually a useful starting point for understanding and exploring data, conventional clustering algorithms are not designed for handling trajectory data. Therefore, in this paper, we propose a direction-based clustering (DEN) method, which aims to group trajectories by moving directions. A key development challenge is how to transform direction information into a data format which is appropriate for traditional clustering algorithms to explore. To this end, we partition the space into grids and turn the movement statistics in a grid into a vector which represents the probabilities of moving directions within the grid. With such data transformation, we are able to develop a grid-level K-means clustering method for direction clustering. We illustrate the use of DEN for showing movement patterns and detecting outliers on real-world data sets.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Information Reuse and Integration, IRI 2010
Pages165-170
Number of pages6
DOIs
StatePublished - 2010
Externally publishedYes
Event11th IEEE International Conference on Information Reuse and Integration, IRI 2010 - Las Vegas, NV, United States
Duration: Aug 4 2010Aug 6 2010

Publication series

Name2010 IEEE International Conference on Information Reuse and Integration, IRI 2010

Conference

Conference11th IEEE International Conference on Information Reuse and Integration, IRI 2010
Country/TerritoryUnited States
CityLas Vegas, NV
Period8/4/108/6/10

Keywords

  • Clustering
  • Data mining
  • Outlier detection
  • Trajectory analysis

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management

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