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

Boo, Yee Ling and Alahakoon, Damminda 2011, Identifying multi-view patterns with hierarchy and granularity based multimodal (HGM) cognitive model, in Proceedings of the 2011 IEEE International Conference on Granular Computing, IEEE, [Kaohsiung, Taiwan], pp. 71-76.

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Title Identifying multi-view patterns with hierarchy and granularity based multimodal (HGM) cognitive model
Author(s) Boo, Yee Ling
Alahakoon, Damminda
Conference name IEEE International Conference on Granular Computing (2011 : Kaohsiung, Taiwan)
Conference location Kaohsiung, Taiwan
Conference dates 8-10 Nov. 2011
Title of proceedings Proceedings of the 2011 IEEE International Conference on Granular Computing
Editor(s) [Unknown]
Publication date 2011
Conference series IEEE International Conference on Granular Computing
Start page 71
End page 76
Publisher IEEE
Place of publication [Kaohsiung, Taiwan]
Keyword(s) granularity
multimodal
hierarchical clustering
growing self organising maps
data mining
Summary 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 (multimodality). 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.
ISBN 9781457703713
9781457703720
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2011, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30043042

Document type: Conference Paper
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