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Additional info for Advanced Techniques in Knowledge Discovery and Data Mining (Advanced Information and Knowledge Processing)
Dissertation, University of Colorado at Boulder, Department of Computer Science, 2003. 54–66, 1997. htm, 2001. htm, July 2000. htm, 2001.  Pazzani, M. , Knowledge discovery from data? 10–3, March/April 2000. , Knowledge discovery in real databases: A report on the IJCAI-89 Workshop, AI Magazine, 11:5, pp. 68–70, Jan. 1991. , (eds), Knowledge Discovery in Databases, AAAI/MIT Press, 1991.  PMML, 2nd Annual Workshop on the Predictive Model Markup Language, San Francisco, CA, August 2001.  Redman, T.
Individual parameters are checked for model consistency with regard to univariate, typically Gaussian assumptions. Further, methods like principal component analysis are used, which by its nature is a linear and parametric approach and, thus, is of limited applicability for nonlinear cases not obeying a multivariate Gaussian model. The signiﬁcant economic potential of the data mining ﬁeld in general and the ﬁeld of semiconductor process data analysis in particular has triggered many activities.
Intelligent Miner for Data uses clustering based on Kohonen neural network, factor analysis, linear and polynomial regression, and decision trees to find associations and patterns in data , , . Intelligent Miner for Text includes a search engine, Web access tools, and text analysis tools. Intelligent Miner Scoring is the DM component designed to work in real time. Intelligent Miner incorporates some data preprocessing methods like feature selection, sampling, aggregation, filtering, cleansing, and data transformations like principal component analysis .