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WI Based Multi-aspect Data Analysis in a Brain Informatics Portal

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Autonomous Intelligent Systems: Multi-Agents and Data Mining (AIS-ADM 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4476))

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Abstract

In order to investigate human information processing mechanism systematically, Web intelligence (WI) based portal techniques are required for brain data measurement, management and analysis. Building a brain informatics portal is, in fact, to develop a data mining grid centric multi-layer grid system on the Wisdom Web, on which various data mining agents are deployed, for multi-aspect data analysis. We propose an approach for collecting, modeling, transforming, managing, and mining multiple human brain data obtained from systematic fMRI/EEG experiments. The proposed approach provides a new way in Brain Informatics (BI) for automatic analysis and understanding of human brain data to replace human-expert centric visualization. We attempt to change the perspective of cognitive scientists from a single type of experimental data analysis towards a holistic view at a long-term, global field of vision to understand the principle, models and mechanisms of human information processing system.

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References

  1. Banquet, J.P.: Spectral Analysis of the EEG in Meditation. Electroencephalography and Clinical Neurophysiology 35, 143–151 (1973)

    Article  Google Scholar 

  2. Banfield, J., Raftery, A.: Model-based Gaussian and Non-Gaussian Clustering. Biometrics 49, 803–821 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  3. Berman, F.: From TeraGrid to Knowledge Grid. CACM 44, 27–28 (2001)

    Google Scholar 

  4. Cannataro, M., Talia, D.: The Knowledge Grid. CACM 46, 89–93 (2003)

    Google Scholar 

  5. Consularo, L.A., Lotufo, R.A., Costa, L.F.: Data Mining Based Modeling of Human Visual Perception. In: Cios, K.J. (ed.) Medical Data Mining and Knowledge Discovery, pp. 403–431. Physica-Verlag, Heidelberg (2001)

    Google Scholar 

  6. Cerutti, S., et al.: A Parametric Method of Identification of Single Trial Event-related Potentials in the Brain. IEEE Trans. Biomed. Eng. 35(9), 701–711 (1988)

    Article  Google Scholar 

  7. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery: an Overview. In: Advances in Knowledge Discovery and Data Mining, pp. 1–36. MIT Press, Cambridge (1996)

    Google Scholar 

  8. Foster, I., Kesselman, C. (eds.): The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  9. Gazzaniga, M.S. (ed.): The Cognitive Neurosciences III. MIT Press, Cambridge (2004)

    Google Scholar 

  10. Handy, T.C.: Event-Related Potentials, A Methods Handbook. MIT Press, Cambridge (2004)

    Google Scholar 

  11. Hornero, R., et al.: A DSP Implementation of Wavelet Transform to Detect Epileptiform Activity in the EEG. In: Proc. 8th Annual International Conference on Signal Processing Applications and Technology, ICAPAT’97, pp. 692–696 (1997)

    Google Scholar 

  12. Hu, J., Zhong, N.: Organizing Multiple Data Sources for Developing Intelligent e-Business Portals. Data Mining and Knowledge Discovery 12(2-3), 127–150 (2006)

    Article  MathSciNet  Google Scholar 

  13. Liu, J., et al.: The Wisdom Web: New Challenges for Web Intelligence (WI). Journal of Intelligent Information Systems 20(1), 5–9 (2003)

    Article  Google Scholar 

  14. Liu, J.: Web Intelligence (WI): What Makes Wisdom Web? In: Proc. 18th International Joint Conference on Artificial Intelligence (IJCAI’03), pp. 1596–1601 (2003)

    Google Scholar 

  15. Megalooikonomou, V., Herskovits, E.H.: Mining Structure-Function Associations in a Brain Image Database. In: Cios, K.J. (ed.) Medical Data Mining and Knowledge Discovery, pp. 153–179. Physica-Verlag, Heidelberg (2001)

    Google Scholar 

  16. Motomura, S., Zhong, N., Wu, J.L.: Brain Waves Data Mining for Human Multi-perception Activity Analysis. In: Proc. Inter. Workshop on Advanced Technologies for e-Learning and e-Science (ATELS’04), pp. 65–72 (2004)

    Google Scholar 

  17. Gazzaniga, M.S., Ivry, R., Mangun, G.R.: Cognitive Neuroscience: The Biology of the Mind, 2nd edn. W.W. Norton, New York (2002)

    Google Scholar 

  18. Mitchell, T.M., et al.: Classifying Instantaneous Cognitive States from fMRI Data. In: Proc. American Medical Informatics Association Annual Symposium, pp. 465–469 (2003)

    Google Scholar 

  19. Newell, A., Simon, H.A.: Human Problem Solving. Prentice-Hall, Englewood Cliffs (1972)

    Google Scholar 

  20. Rosen, B.R., Buckner, R.L., Dale, A.M.: Event-related functional MRI: Past, Present, and Future. Proceedings of National Academy of Sciences, USA 95(3), 773–780 (1998)

    Article  Google Scholar 

  21. Sai, Y., Yao, Y.Y., Zhong, N.: Data Analysis and Mining in Ordered Information Tables. In: Proc. 2001 IEEE International Conference on Data Mining (ICDM’01), pp. 497–504. IEEE Computer Society Press, Los Alamitos (2001)

    Google Scholar 

  22. Sternberg, R.J., Lautrey, J., Lubart, T.I.: Models of Intelligence. American Psychological Association (2003)

    Google Scholar 

  23. Su, Y., et al.: A Method of Distributed Problem Solving on the Web. In: Proc. 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI’05), pp. 42–45. IEEE Computer Society Press, Los Alamitos (2005)

    Google Scholar 

  24. Su, Y., et al.: Distributed Reasoning Based on Problem Solver Markup Language (PSML): A Demonstration through Extended OWL. In: Proc. 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE’05), pp. 208–213. IEEE Computer Society Press, Los Alamitos (2005)

    Google Scholar 

  25. Tomita, K., Zhong, N., Yamauchi, H.: Coupling Global Semantic Web with Local Information Sources for Problem Solving. In: Proc. First International Workshop on Semantic Web Mining and Reasoning, pp. 66–74 (2004)

    Google Scholar 

  26. Tsukimoto, H., Morita, C.: The Discovery of Rules from Brain Images. In: Arikawa, S., Motoda, H. (eds.) DS 1998. LNCS (LNAI), vol. 1532, pp. 198–209. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  27. Zhong, N., Ohsuga, S.: Toward A Multi-Strategy and Cooperative Discovery System. In: Proc. First Int. Conf. on Knowledge Discovery and Data Mining (KDD-95), pp. 337–342. AAAI Press, Menlo Park (1995)

    Google Scholar 

  28. Zhong, N., Ohsuga, S.: Automatic Knowledge Discovery in Larger Scale Knowledge-Data Bases. In: Leondes, C. (ed.) The Handbook of Expert Systems, vol. 4, pp. 1015–1070. Academic Press, London (2001)

    Google Scholar 

  29. Zhong, N., Liu, C., Ohsuga, S.: Dynamically Organizing KDD Process. International Journal of Pattern Recognition and Artificial Intelligence 15(3), 451–473 (2001)

    Article  Google Scholar 

  30. Zhong, N., Dong, J.Z., Ohsuga, S.: Rule Discovery by Soft Induction Techniques. Neurocomputing 36(1–4), 171–204 (2001)

    Article  MATH  Google Scholar 

  31. Zhong, N., Liu, J., Yao, Y.Y.: In Search of the Wisdom Web. IEEE Computer 35(11), 27–31 (2002)

    Google Scholar 

  32. Zhong, N., et al.: Gastric Cancer Data Mining with Ordered Information. In: Alpigini, J.J., et al. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 467–478. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  33. Zhong, N., Yao, Y.Y., Ohshima, M.: Peculiarity Oriented Multi-Database Mining. IEEE Transaction on Knowlegde and Data Engineering 15(4), 952–960 (2003)

    Article  Google Scholar 

  34. Zhong, N.: Developing Intelligent Portals by Using WI Technologies. In: Li, J.P., et al. (eds.) Wavelet Analysis and Its Applications, and Active Media Technology, vol. 2, pp. 555–567. World Scientific, Singapore (2004)

    Google Scholar 

  35. Zhong, N., et al.: Peculiarity Oriented fMRI Brain Data Analysis for Studying Human Multi-Perception Mechanism. Cognitive Systems Research 5(3), 241–256 (2004)

    Article  Google Scholar 

  36. Zhong, N., et al.: Building a Data Mining Grid for Multiple Human Brain Data Analysis. Computational Intelligence 21(2), 177–196 (2005)

    Article  MathSciNet  Google Scholar 

  37. Zhong, N., Motomura, S., Wu, J.L.: Peculiarity Oriented Multi-Aspect Brain Data Analysis for Studying Human Multi-Perception Mechanism. In: Proc. SAINT 2005 Workshops (Workshop 8: Computer Intelligence for Exabyte Scale Data Explosion), pp. 306–309. IEEE Computer Society Press, Los Alamitos (2005)

    Google Scholar 

  38. Zhong, N.: Impending Brain Informatics (BI) Research from Web Intelligence (WI) Perspective. International Journal of Information Technology and Decision Making 5(4), 713–727 (2006)

    Article  Google Scholar 

  39. Zhong, N., Liu, J., Yao, Y.Y.: Envisioning Intelligent Information Technologies (iIT) from the Stand-Point of Web Intelligence (WI). Communications of the ACM 50(3) (2007)

    Google Scholar 

  40. The OGSA-DAI project: http://www.ogsadai.org.uk/

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Author information

Authors and Affiliations

  1. The International WIC Institute/BJUT,  

    Ning Zhong

  2. Department of Life Science and Informatics, Maebashi Institute of Technology, 460-1 Kamisadori-cho, Maebashi 371-0816, Japan

    Ning Zhong & Shinichi Motomura

Authors
  1. Ning Zhong
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  2. Shinichi Motomura
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Editor information

Vladimir Gorodetsky Chengqi Zhang Victor A. Skormin Longbing Cao

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Zhong, N., Motomura, S. (2007). WI Based Multi-aspect Data Analysis in a Brain Informatics Portal. In: Gorodetsky, V., Zhang, C., Skormin, V.A., Cao, L. (eds) Autonomous Intelligent Systems: Multi-Agents and Data Mining. AIS-ADM 2007. Lecture Notes in Computer Science(), vol 4476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72839-9_4

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  • DOI: https://doi.org/10.1007/978-3-540-72839-9_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72838-2

  • Online ISBN: 978-3-540-72839-9

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