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Identifying multi-view patterns with hierarchy and granularity based multimodal (HGM) cogntive model

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2 Author(s)
Yee Ling Boo ; School of Information Systems, Faculty of Business and Law, Deakin University, Burwood, Victoria, Australia ; Damminda Alahakoon

Humans perceive entities such as objects, patterns, events, etc. as concepts, which are the basic units in human intelligence and communications. In addition, perceptions of these entities could be abstracted and generalised at multiple levels of granularity. In particular, such granulation allows the formation and usage of concepts in human intelligence. Such natural granularity in human intelligence could inspire and motivate the design and development of pattern identification approach in Data Mining. In our opinion, a pattern could be perceived at multiple levels of granularity and thus we advocate for the co-existence of hierarchy and granularity. In addition, granular patterns exist across different sources of data (mul-timodality). In this paper, we present a cognitive model that incorporates the characteristics of Hierarchy, Granularity and Multimodality for multi-view patterns identification in crime domain. Such framework is implemented with Growing Self Organising Maps (GSOM) and some experimental results are presented and discussed.

Published in:

Granular Computing (GrC), 2011 IEEE International Conference on

Date of Conference:

8-10 Nov. 2011