Data Mining and Knowledge Discovery Handbook (Springer series in solid-state sciences)

Data Mining and Knowledge Discovery Handbook (Springer series in solid-state sciences)

Lior Rokach

Language: English

Pages: 1285

ISBN: 0387098224

Format: PDF / Kindle (mobi) / ePub


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.

Software Engineering 2: Specification of Systems and Languages (Texts in Theoretical Computer Science. An EATCS Series)

A Discipline of Multiprogramming: Programming Theory for Distributed Applications (Monographs in Computer Science)

Fundamentals of Database Systems (7th Edition)

Credibilistic Programming: An Introduction to Models and Applications (Uncertainty and Operations Research)

 

 

 

 

 

 

 

 

 

 

 

 

Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 315–319. Bluman, A. G. (1992). Elementary Statistics, A Step By Step Approach. Wm.C.Brown Publishers. page5-8. Catlett, J. (1991). On changing continuous attributes into ordered discrete attributes. In Proceedings of the European Working Session on Learning, pages 164–178. Chmielewski, M. R. and Grzymala-Busse, J. W. (1996). Global discretization of continuous attributes as preprocessing for

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

Download sample

Download