REFERENCES

data mining: opportunities and challenges
Chapter III - Cooperative Learning and Virtual Reality-Based Visualization for Data Mining
Data Mining: Opportunities and Challenges
by John Wang (ed) 
Idea Group Publishing 2003
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Adriaans, P. & Zantinge, D. (1996). Data mining. Harlowi, UK: Addison Wesley.

Agrawal, R., Imielinski, T., & Swami, A. (1993). Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering, 5(6): 914 25. December.

Angel, E. (1997).Interactive computer graphics, a top down approach with OpenGL. New York: Addison Wesley.

Becker, B., Kohavi, R., & Sommerfield, S. (2001). Visualizing the simple Bayesian classifier. In U. Fayyad, G.G. Grinstein & A. Wierse (eds.), Information visualization in data mining and knowledge discovery, pp.237 250. San Francisco: Morgan Kaufmann.

Begault, D.R. (1994). 3D sound for virtual reality and multimedia. New York: Academic Press.

Burdea, G.C. (1996). Force and touch feedback for virtual reality. New York: Wiley Interscience.

Carter, C.L. & Hamilton, H.J. (1997). Efficient attribute-oriented algorithms for knowledge discovery from large databases. IEEE Transactions on Knowledge and Data Engineering 10(2), pp. 193 208.

Chakrabarti, K. & Mehrotra, S. (2000). Local dimensionality reduction: A new approach to indexing high dimensional spaces. In Proceedings of Very Large Data Bases - VLDB'00, Cairo, Egypt, pp. 89 100, September.

Churchill, E.F., Snowdon, D. N. & Munro, A. J. (2001). Collaborative virtual environments. Berlin: Springer-Verlag.

Clark, P. & Niblett, T. (1989). The CN2 induction algorithm. Machine Learning, 3: 261 283.

Cruz-Neira, C., Sandin, D. & Defanti, T. (1993). Surround-screen projection-based virtual reality. In Proceedings of The Design and Implementation of the CAVE, SIGGRAPH'93, Anaheim, California, pp.135 142. August.

Docherty, P. & Beck, A. (2001). A visual metaphor for knowledge discovery: An integrated approach to visualizing the task, data and results. In U. Fayyad, G. G. Grinstein & A. Wierse (eds.), Information vizualization in data mining and knowledge discovery, pp.191 204. San Francisco: Morgan Kaufmann.

Fayyad, U., Grinstein, G. G., & Wierse, A. (2001). Information visualization in data mining and knowledge discovery. San Francisco: Morgan Kaufmannn.

Fischer, M., Scholten, H.J., & Unwin, D. (1996). Spatial analytical perspectives on GIS. GISDATA 4, London: Taylor & Francis.

Foong, D.L.W. (2001). A visualization-driven approach to strategic knowledge discovery. In U. Fayyad, G.G. Grinstein & A. Wierse (eds.), Information visualization in data mining and knowledge discovery, pp.181 190. San Francisco: Morgan Kaufmann.

Foster, M. & Gee, A.G. (2002). The data visualization environment. In U. Fayyad, G.G. Grinstein & A. Wierse (eds.), Information visualization in data mining and knowledge discovery, pp.83 94. San Francisco: Morgan Kaufmann.

Ganesh, M., Han, E.H., Kumar, V., Shekar, S., & Srivastava, J. (1996). Visual data mining: Framework and algorithm development. Working Paper. Twin Cities, MN: University of Minnesota, Twin Cities Campus.

Gershon, N. & Eick, S.G. (1995). Visualization's new tack: Making science of Information. IEEE Spectrum, November, pp.38 56.

Grinstein, G.G. & Ward, M.O. (2001). Introduction to data visualization. In U. Rayyad, G.G. Grinstein, & A. Wierse (eds.). Information visualization in data mining and knowledge discovery, pp.21 26. San Francisco: Morgan Kaufmann.

Han, J. & Kamber, M. (2001). Data mining concepts and techniques. San Francisco: Morgan Kaufmann.

Harris, R.L. (2000). Information graphics: A comprehensive illustrated reference. Oxford, UK: Oxford University Press.

Hilderman, R.J., Li, L., & Hamilton, H.J. (2001). Visualizing data mining results with domain generalization graphs. In U. Fayyad, G. Grinstein & A. Wierse (eds.), Information visualization in data mining and knowledge discovery, pp.251 269. San Francisco: Morgan Kaufmann.

Hinke, T.H. & Newman, T.S. (2001). A taxonomy for integrating data mining and data visualization. In U. Fayyad, G. Grinstein & A. Wierse (eds.), Information visualization in data mining and knowledge discovery, pp. 291 298. San Francisco: Morgan Kaufmann.

Hoffman, P.E. & Grinstein, G.G. (2001). A survey of visualization for high-dimensional data mining. In U. Fayyad, G.G. Grinstein, & A. Wierse (eds.), Information visualization in data mining and knowledge discovery, pp.47 82. San Francisco: Morgan Kaufmann.

Honavar, V. (1995). Symbolic artificial intelligence and numeric artificial neural networks: Towards a resolution of dichotomy, computational architectures integrating neural and symbolic processes. Boston, MA: Kluwer Academic Publishers.

Johnson-Laird, P. (1993). The computer and the mind: An introduction to cognitive science (2nd ed.). London: Fontana Masterguides.

Keim, D.A. & Kriegel, H.P. (1995). Issues in visualizing large databases. In Visual Information Management, Proceedings of the 3rd IFIP 2.6 Working Conference on Visual Database Systems. London, UK: Chapman and Hall, pp. 203 14.

Keim, D.A., Lee, J.P., Thuraisinghaman, B., & Wittenbrink, C. (1998). Database issues for data visualization: Supporting interactive database exploration. IEEE Visualization '95 Workshop, Proceedings. Berlin, Germany:. Springer-Verlag, pp. 12 25.

Lin, L. & Hendler, S. (1995). Examining a hybrid connectionist/symbolic system for the analysis of ballistic signals. In R. Sun (ed.), Computational Architectures Integrating Neural and Symbolic Processes, pp.113 130. Boston, MA: Kluwer Academic Press.

Mitchell, T. (1997). Machine learning. New York: McGraw-Hill.

Multiple Authors (2000). Special issue on large wall displays. IEEE Computer Graphics and Applications, 20(4).

O'Rourke, (1998). Principles of three-dimensional computer animation Modeling, rendering & animating with 3D computer graphics (Revised Ed.). New York: W. W. Norton & Company, ISBN: 0393730247.

Paquet, E, Robinette, K.M., & Rioux, M. (2000). Management of three-dimensional and anthropometric databases: Alexandria and Cleopatra. Journal of Electronic Imaging, 9, 421 431.

Pretorius, J. (2001). Using geographic information systems for data mining. Working paper. University of Pretoria, South Africa.

Pyle, D. (1999). Data preparation for data mining. San Francisco: Morgan Kaufmann.

Quinlan, R. (1994). C4.5: Programs for machine learning. San Francisco: Morgan Kaufmann.

Redman, T.C. (1996). Data quality for the information age. Norwood, MA: Artech House.

Reinsel, G.C. & Velu, R.P. (1998). Multivariate reduced rank regression: Theory and applications. Berlin: Springer-Verlag.

Ruhle, R., Land, U., & Wierse, A. (1993). Cooperative visualization and simulation in a supercomputer environment. In Proceedings of the Joint International Conference on Mathematical Methods and Supercomputing in Nuclear Applications 2, April 19 23, pp.530 541.

Senator, T.E., Goldberg, H.G., & Shyr, P. (2001). The NASD regulation advanced detection system. In U. Fayyad, G.G. Grinstein, & A. Wierse (eds.), Information visualization in data mining and knowledge discovery, pp.363 371. San Francisco: Morgan Kaufmann.

Singhal, S. & Zyda, M. (1999). Networked virtual environments: Design and implementation. Reading, MA: Addison Wesley.

Sun, R. (1995). Computational architectures iIntegrating neural and symbolic processes. An introduction: On symbolic processing in neural networks, pp. 1 21. Boston, MA: Kluwer Academic Press.

Thearling, K., Becker, B., DeCoste, D., Mawby, W. D., Pilote, M. & Sommerfield, D. (2001). Visualizing data mining models. In U. Fayyad, G.G. Grinstein, & A. Wierse (eds.), Information visualization in data mining and knowledge discovery, pp.205 222. San Francisco: Morgan Kaufmann.

Tufte, E. (1990). The visual display of quantitative information. Cheshire, CT: Graphics Press.

Viktor, H.L. (1999). The CILT multi-agent learning system. South African Computer Journal (SACJ), 24, 171 181.

Viktor, H.L., Engelbrecht, A.P., & Cloete, I. (1998). Incorporating rule extraction from artificial neural networks into a computational network. In Proceedings of the International Conference on Neural Networks and Their Applications (NEURAP'98), March 11 13, Marseille: France, pp.421 429.

Viktor, H.L., le Roux, J.G., & Paquet, E. (2001). The ViziMine visual data mining tool. In Proceedings of International Conference on Advances in Infrastructure for Electronic Business, Science, and Education on the Internet (SSGRR'2001), L'Aquila: Italy, August 6 11, CD-ROM.

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Data Mining(c) Opportunities and Challenges
Data Mining: Opportunities and Challenges
ISBN: 1591400511
EAN: 2147483647
Year: 2003
Pages: 194
Authors: John Wang

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