From Data Mining to Knowledge Discovery in Databases

We hope this will eventually lead to a better

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Unformatted text preview: ommon to find that they share many common components. Understanding data mining and model induction at this component level clarifies the task of any data-mining algorithm and makes it easier for the user to understand its overall contribution and applicability to the KDD process. This article represents a step toward a common framework that we hope will ultimately provide a unifying vision of the common overall goals and methods used in KDD. We hope this will eventually lead to a better understanding of the variety of approaches in this multidisciplinary field and how they fit together. Note 1. Throughout this article, we use the term pattern to designate a pattern found in data. We also refer to models. One can think of patterns as components of models, for example, a particular rule in a classification model or a linear component in a regression model. References Agrawal, R., and Psaila, G. 1995. Active Data Mining. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), 3–8. Menlo Park, Calif.: American Association for Artificial Intelligence. Agrawal, R.; Mannila, H.; Srikant, R.; Toivonen, H.; and Verkamo, I. 1996. Fast Discovery of Association Rules. In Advances in Knowledge Discovery and Data Mining, eds. U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 307–328. Menlo Park, Calif.: AAAI Press. Apte, C., and Hong, S. J. 1996. Predicting Equity Returns from Securities Data with Minimal Rule Generation. In Advances in Knowledge Discovery and Data Mining, eds. U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 514–560. Menlo Park, Calif.: AAAI Press. Basseville, M., and Nikiforov, I. V. 1993. Detection of Abrupt Changes: Theory and Application. Englewood Cliffs, N.J.: Prentice Hall. Berndt, D., and Clifford, J. 1996. Finding Patterns in Time Series: A Dynamic Programming Approach. In Advances in Knowledge Discovery and Data Mining, eds. U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 229–248. Menlo Park, Calif.: AAAI Press. Berry, J. 1994. Database Marketing. Business Week, September 5, 56–62. Brachman, R., and Anand, T. 1996. The Process of Knowledge Discovery in Databases: A Human-Centered Approach. In Advances in Knowledge Discovery and Data Mining, 37–58, eds. U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Menlo Park, Calif.: AAAI Press. Breiman, L.; Friedman, J. H.; Olshen, R. A.; and Stone, C. J. 1984. Classification and Regression Trees. Belmont, Calif.: Wadsworth. Brodley, C. E., and Smyth, P. 1996. Applying Classification Algorithms in Practice. Statistics and Computing. Forthcoming. Buntine, W. 1996. Graphical Models for Discovering Knowledge. In Advances in Knowledge Discovery and Data Mining , eds. U. Fayyad, G. PiatetskyShapiro, P. Smyth, and R. Uthurusamy, 59–82. Menlo Park, Calif.: AAAI Press. Acknowledgments Cheeseman, P. 1990. On Finding the Most Probable Model. In Computational Models of Scientific Discove...
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This document was uploaded on 02/15/2014.

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