Clusters driven implementation of a brain inspired model for multi-view pattern identifications

Boo, Yee Ling and Alahakoon, Damminda 2011, Clusters driven implementation of a brain inspired model for multi-view pattern identifications, in ISDA 2011 : Proceedings of the 11th International Conference on Intelligent Systems Design and Applications, IEEE, [Cordoba, Spain], pp. 551-556.

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Title Clusters driven implementation of a brain inspired model for multi-view pattern identifications
Author(s) Boo, Yee Ling
Alahakoon, Damminda
Conference name Intelligent Systems Design and Applications. Conference (11th : 2011 : Cordoba, Spain)
Conference location Cordoba, Spain
Conference dates 22-24 Nov. 2011
Title of proceedings ISDA 2011 : Proceedings of the 11th International Conference on Intelligent Systems Design and Applications
Editor(s) [Unknown]
Publication date 2011
Conference series Intelligent Systems Design and Applications Conference
Start page 551
End page 556
Total pages 6
Publisher IEEE
Place of publication [Cordoba, Spain]
Keyword(s) multimodal
granularity
hierarchical clustering
growing self organising maps
data mining
Summary The human brain processes information in both unimodal and multimodal fashion where information is progressively captured, accumulated, abstracted and seamlessly fused. Subsequently, the fusion of multimodal inputs allows a holistic understanding of a problem. The proliferation of technology has produced various sources of electronic data and continues to do so exponentially. Finding patterns from such multi-source and multimodal data could be compared to the multimodal and multidimensional information processing in the human brain. Therefore, such brain functionality could be taken as an inspiration to develop a methodology for exploring multimodal and multi-source electronic data and further identifying multi-view patterns. In this paper, we first propose a brain inspired conceptual model that allows exploration and identification of patterns at different levels of granularity, different types of hierarchies and different types of modalities. Secondly, we present a cluster driven approach for the implementation of the proposed brain inspired model. Particularly, the Growing Self Organising Maps (GSOM) based cross-clustering approach is discussed. Furthermore, the acquisition of multi-view patterns with clusters driven implementation is demonstrated with experimental results.
ISBN 9781457716751
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:30043041

Document type: Conference Paper
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