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Issue No.03 - May/June (2009 vol.24)
pp: 38-45
Ning Zhong , Maebashi Institute of Technology, Japan
Shinichi Motomura , Maebashi Institute of Technology, Japan
The brain informatics methodology supports systematic brain data measurement, management, and analysis. An agent-enriched peculiarity-oriented mining approach offers a case study of the BI method by transforming and mining human-brain data obtained from cognitive event-related potential experiments.
human-brain ERP data mining, agent-enriched knowledge discovery, brain informatics
Ning Zhong, Shinichi Motomura, "Agent-Enriched Data Mining: A Case Study in Brain Informatics", IEEE Intelligent Systems, vol.24, no. 3, pp. 38-45, May/June 2009, doi:10.1109/MIS.2009.46
1. R. Morris, L. Tarassenko, and M. Kenward eds., Cognitive Systems: Information Processing Meets Brain Science, Elsevier, 2006.
2. N. Zhong, "Impending Brain Informatics (BI) Research from Web Intelligence (WI) Perspective," , Int'l J. Information Technology and Decision Making, vol. 5, no. 4, 2006, pp. 713–727.
3. T.M. Mitchell et al., "Predicting Human Brain Activity Associated with the Meanings of Nouns," Science, vol. 320, 2008, pp. 1191–1195.
4. T.C. Handy, Event-Related Potentials, A Methods Handbook, MIT Press, 2004.
5. F.T. Sommer and A. Wichert eds., , Exploratory Analysis and Data Modeling in Functional Neuroimaging, MIT Press, 2003.
6. N. Zhong and S. Motomura, "WI Based Multi-Aspect Data Analysis in a Brain Informatics Portal," Autonomous Intelligent Systems: Agents and Data Mining, V. Gorodetsky et al., eds., LNAI 4476, Springer, 2007, pp. 46–59.
7. N. Zhong et al., "Web Intelligence Meets Brain Informatics," N. Zhong et al., eds., Web Intelligence Meets Brain Informatics, LNAI 4845, Springer, 2007, pp. 1–31.
8. N. Zhong et al., "Peculiarity Oriented fMRI Brain Data Analysis for Studying Human Multi-Perception Mechanism," Cognitive Systems Research, vol. 5, no. 3,Elsevier, 2004, pp. 241–256.
9. N. Zhong, J. Liu, and Y.Y. Yao, "En-visioning Intelligent Information Tech-nologies through the Prism of Web In-telligence," Comm. ACM, vol. 50, no. 3, 2007, pp. 89–94.
10. N. Zhong et al., "Building a Data Mining Grid for Multiple Human Brain Data Analysis," Computational Intelligence, vol. 21, no. 2,Blackwell, 2005, pp. 177–196.
11. M.S. Gazzaniga ed., The Cognitive Neurosciences III, MIT Press, 2004.
12. J.R. Anderson, How Can the Human Mind Occur in the Physical Universe?, Oxford Univ. Press, 2007.
13. H. Nittono et al., "Event-Related Potential Correlates of Individual Differences in Working Memory Capacity," Psychophysiology, vol. 36, no. 6, 1999, pp. 745–754.
14. G. Dong and J. Li, "Efficient Mining of Emerging Patterns: Discovering Trends and Differences," Proc. 5th Int'l Conf. Knowledge Discovery and Data Mining (KDD 99), AAAI Press, 1999, pp. 43–52.
15. A.A. Freitas, "On Objective Measures of Rule Surprisingness," J. Zytkow, and M. Quafafou eds., , Principles of Data Mining and Knowledge Discovery, LNAI 1510, Springer, 1998, pp. 1–9.
16. B. Liu et al., "Analyzing the Subjective Interestingness of Association Rules," IEEE Intelligent Systems, vol. 15, no. 5, 2000, pp. 47–55.
17. E. Suzuki, "Autonomous Discovery of Reliable Exception Rules," Proc. 3rd Int'l Conf. Knowledge Discovery and Data Mining (KDD 97), AAAI Press, 1997, pp. 259–262.
18. N. Zhong, Y.Y. Yao, and M. Ohshima, "Peculiarity Oriented Multidatabase Mining," IEEE Trans. Knowledge Data Eng., vol. 15, no. 4, 2003, pp. 952–960.
19. L.I. Perlovsky, "Toward Physics of the Mind: Concepts, Emotions, Consciousness, and Symbols," Phys. Life Rev., vol. 3, no. 1, 2006, pp. 23–55.
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