Mining multi-modal crime patterns at different levels of granularity using hierarchical clustering

Boo, Yee Ling and Alahakoon, Damminda 2008, Mining multi-modal crime patterns at different levels of granularity using hierarchical clustering, in CIMCA 2008 : Proceeding of the 2008 International Conference on Computational Intelligence for Modelling, Control and Automation, December 10-12, 2008, Vienna, Austria, IEEE, Piscataway, N.J., pp. 1268-1273.

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Title Mining multi-modal crime patterns at different levels of granularity using hierarchical clustering
Author(s) Boo, Yee Ling
Alahakoon, Damminda
Conference name Computational Intelligence for Modelling, Control and Automation. Conference (2008 : Vienna, Austria)
Conference location Vienna, Austria
Conference dates 10-12 Dec. 2008
Title of proceedings CIMCA 2008 : Proceeding of the 2008 International Conference on Computational Intelligence for Modelling, Control and Automation, December 10-12, 2008, Vienna, Austria
Editor(s) [Unknown]
Publication date 2008
Conference series Computational Intelligence for Modelling, Control and Automation Conference
Start page 1268
End page 1273
Publisher IEEE
Place of publication Piscataway, N.J.
Summary The appearance of patterns could be found in different modalities of a domain, where the different modalities refer to the data sources that constitute different aspects of a domain. Particularly, the domain of our discussion refers to crime and the different modalities refer to the different data sources such as offender data, weapon data, etc. in crime domain. In addition, patterns also exist in different levels of granularity for each modality. In order to have a thorough understanding a domain, it is important to reveal the hidden patterns through the data explorations at different levels of granularity and for each modality. Therefore, this paper presents a new model for identifying patterns that exist in different levels of granularity for different modes of crime data. A hierarchical clustering approach - growing self organising maps (GSOM) has been deployed. Furthermore, the model is enhanced with experiments that exhibit the significance of exploring data at different granularities.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 9780769535142
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30033066

Document type: Conference Paper
Collection: School of Information and Business Analytics
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