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

Content Provider	IEEE Xplore Digital Library
Author	Yee Ling Boo Alahakoon, D.
Copyright Year	2011
Description	Author affiliation: School of Information Systems, Faculty of Business and Law, Deakin University, Burwood, Victoria, Australia (Yee Ling Boo) \|\| Cognitive and Connectionist Systems Lab (CCSL), Faculty of Information Technology, Monash University, Clayton, Victoria, Australia (Alahakoon, D.)
Abstract	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.
Starting Page	551
Ending Page	556
File Size	291951
Page Count	6
File Format	PDF
ISBN	9781457716768
ISSN	21647151
e-ISBN	9781457716768
DOI	10.1109/ISDA.2011.6121713
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-22
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Growing Self Organising Maps Biological system modeling Multimodal Humans Brain modeling Data Mining Vectors Hierarchical Clustering Data mining Granularity Intelligent systems Pattern matching
Content Type	Text
Resource Type	Article

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