By John Wang
Information Mining: possibilities and demanding situations provides an outline of the cutting-edge techniques during this new and multidisciplinary box of information mining. the first aim of this publication is to discover the myriad matters concerning information mining, in particular targeting these parts that discover new methodologies or study case reports. This ebook comprises a number of chapters written by means of a global crew of forty-four specialists representing prime scientists and gifted younger students from seven assorted nations.
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Info Mining: possibilities and demanding situations offers an outline of the cutting-edge methods during this new and multidisciplinary box of information mining. the first aim of this publication is to discover the myriad matters concerning facts mining, particularly targeting these parts that discover new methodologies or learn case experiences.
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Additional resources for Data Mining: Opportunities and Challenges
1999). Computation, causation and discovery. Cambridge, MA: MIT Press. , Larsson, S. (1999). org. & Smyth, P. (2001). Principles of data mining. Cambridge, MA: MIT Press. Heckerman, D. (1997). Bayesian networks for data mining. Data Mining and Knowledge Discovery 1, 79-119. Jaynes, E. T. (2003). Probability theory: The logic of science. Cambridge University Press, ISBN: 0521592712. Copyright © 2003, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc.
Is prohibited. Control of Inductive Bias in Supervised Learning 35 selection (Kohavi & John, 1997), is highly relevant ,though directed toward a different goal for problem reformulation; this section outlines differences between subset selection and partitioning and how partitioning may be applied to task decomposition. Second, this chapter compares top-down, bottom-up, and hybrid approaches for attribute partitioning, and considers the role of partitioning in feature extraction from heterogeneous time series.
Problems: Attribute Subset Selection and Partitioning This section introduces the attribute partitioning problem and a method for subproblem definition in multi-attribute inductive learning. Attribute-Driven Problem Decomposition for Composite Learning Many techniques have been studied for decomposing learning tasks to obtain more tractable subproblems and to apply multiple models for reduced variance. This section examines attribute-based approaches for problem reformulation, which start with restriction of the set of input attributes on which the supervised learning algorithms will focus.