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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CIMCA 2008 : Proceeding of the 2008 International Conference on Computational Intelligence for Modelling, Control and Automation, December 10-12, 2008, Vienna, Austria
Computational Intelligence for Modelling, Control and Automation Conference
Place of publication
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.
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Field of Research
080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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