Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30840
A Brain Inspired Approach for Multi-View Patterns Identification

Authors: Damminda Alahakoon, Yee Ling Boo


Biologically human brain processes information in both unimodal and multimodal approaches. In fact, information is progressively abstracted and seamlessly fused. Subsequently, the fusion of multimodal inputs allows a holistic understanding of a problem. The proliferation of technology has exponentially produced various sources of data, which could be likened to being the state of multimodality in human brain. Therefore, this is an inspiration to develop a methodology for exploring multimodal data and further identifying multi-view patterns. Specifically, we 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. A structurally adaptive neural network is deployed to implement the proposed model. Furthermore, the acquisition of multi-view patterns with the proposed model is demonstrated and discussed with some experimental results.

Keywords: Data Mining, Multimodal, Hierarchical Clustering, granularity, Growing Self Organising Maps

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1237


[1] C. B. Saper, S. Iversen, and R. Frackowiak, "Integration of sensory and motor function: The association areas of the cerebral cortex and the cognitive capabilities of the brain," in Principles of neural science, 4th ed., E. R. Kandel, J. H. Schwartz, and T. M. Jessell, Eds. New York: McGraw-Hill, 2000, ch. 19, pp. 349-380.
[2] V. Mountcastle, "The columnar organization of the neocortex." Brain, no. 120, pp. pp. 701-722, 1997.
[3] K. Friston, "Hierarchical models in the brain," PLoS Computational Biology, vol. 4, no. 11, 2008.
[4] D. L. Hall and J. Llinas, "An introduction to multisensor data fusion," Proceedings of the IEEE, vol. 85, no. 1, pp. pp. 6-23, 1997.
[5] J. R. Hobbs, "Granularity," in Proceedings of the 9th International Joint Conference on Artificial Intelligence (IJCAI). Los Angeles, USA: Morgan Kaufmann, 1985, pp. 432-435.
[6] Y. Yao, "Perspectives of granular computing," in IEEE International Conference on Granular Computing (GrC), Beijing, China, 2005, pp. 85-90.
[7] Y. Chen and Y. Yao, "A multiview approach for intelligent data analysis based on data operators," International Journal of Information Sciences, vol. 178, pp. pp. 1-20, 2008.
[8] ÔÇöÔÇö, "Multiview intelligent data analysis based on granular computing," in IEEE International Conference on Granular Computing (GrC), Atlanta, USA, 2006, pp. 281-286.
[9] M. Minsky, The Emotion Machine : commensense thinking, artificial intelligence, and the future of the human mind. New York: Simon & Schuster, 2006.
[10] S. Zhang, C. Zhang, and X. Wu, Knowledge Discovery in Multiple Databases. London, UK: Springer-Verlag, 2004.
[11] N. Kasabov, E. Postma, and J. van den Herik, "Avis: a connectionistbased framework for integrated auditory and visual information processing," Information Sciences, vol. 123, pp. pp. 127-148, 2000.
[12] N. Kasabov, "Evolving systems for integrated multi-modal information processing," in Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machine. London: Springer-Verlag, 2003, ch. 13, pp. 257-271.
[13] ÔÇöÔÇö, "Evolving intelligent systems for adaptive multimodal information processing," in Evolving Connectionist Systems The Knowledge Engineering Approach. London: Springer-Verlag, 2007, ch. 13, pp. 361- 380.
[14] J. Hawkins and S. Blakeslee, On Intelligence. New York: Times Books, 2004.
[15] J. Hawkins and D. George, "Hierarchical temporal memory - concepts, theory, and terminology," Numenta Inc., Redwood City, California, White Paper, 2007.
[16] Y. Lu, "Concept hierarchy in data mining: Specification,generation and implementation," Master-s thesis, Simon Fraser University, Canada, 1997.
[17] D. Alahakoon, S. K. Halgamuge, and B. Srinivasan, "A self growing cluster development approach to data mining," in IEEE Conference Systems, Man and Cybernetics, San Diego, USA, 1998, pp. 2901-2906.
[18] ÔÇöÔÇö, "Dynamic self-organizing maps with controlled growth for knowledge discovery," IEEE Transactions on Neural Networks, vol. 11, no. 3, pp. pp. 601-614, 2000.
[19] R. S. Forsyth, "Uci machine learning repository, zoo data set," 1990. (Online). Available: