Data Mining and Knowledge Discovery Handbook (Springer series in solid-state sciences)
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This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.
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very large sets of varied types of input data. The notion, “scalability” usually refers to datasets that fulﬁll at least one of the following properties: high number of records or high dimensionality. “Classical” induction algorithms have been applied with practical success in many relatively simple and small-scale problems. However, trying to discover knowledge in real life and large databases, introduces time and memory problems. As large databases have become the norm in many ﬁelds (including
the input attribute and the target attribute are conditionally independent. If H0 holds, the test statistic is distributed as χ 2 with degrees of freedom equal to: (dom(ai ) − 1) · (dom(y) − 1). 9.3.6 DKM Criterion The DKM criterion is an impurity-based splitting criterion designed for binary class attributes (Dietterich et al., 1996) and (Kearns and Mansour, 1999). The impuritybased function is deﬁned as: DKM(y, S) = 2 · σy=c1 S |S| · σy=c2 S |S| It has been theoretically proved (Kearns and
cells using a binary tree classiﬁer. Pattern Recognition, 16(1):69-80, 1983. Loh W.Y.,and Shih X., Split selection methods for classiﬁcation trees. Statistica Sinica, 7: 815-840, 1997. Loh W.Y. and Shih X., Families of splitting criteria for classiﬁcation trees. Statistics and Computing 9:309-315, 1999. Loh W.Y. and Vanichsetakul N., Tree-structured classiﬁcation via generalized discriminant Analysis. Journal of the American Statistical Association, 83: 715-728, 1988. Lopez de Mantras R., A
graph and a probability distribution. Nodes in the directed acyclic graph represent stochastic variables and arcs represent directed dependencies among variables that are quantiﬁed by conditional probability distributions. As an example, consider the simple scenario in which two variables control the value of a third. We denote the three variables with the letters A, B and C, and we assume that each is bearing two states: “True” and “False”. The Bayesian network in Figure 10.1 describes the