Evolutionary Computation in Data Mining (Hardcover, 2005 ed.)

Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation).

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Studies in Fuzziness and Soft Computing, 163
Release date:	October 2004
First published:	2005
Editors:	Ashish Ghosh
Dimensions:	235 x 155 x 17mm (L x W x T)
Format:	Hardcover
Pages:	266
Edition:	2005 ed.
ISBN-13:	978-3-540-22370-2
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-540-22370-3
Barcode:	9783540223702

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