Articles liés à Evolutionary Computation In Data Mining
Synopsis
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).
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- ÉditeurSpringer-Verlag Berlin and Heidelberg GmbH & Co. K
- Date d'édition2004
- ISBN 10 3540223703
- ISBN 13 9783540223702
- ReliureRelié
- Langueanglais
- Nombre de pages266
- ÉditeurGhosh Ashish
- Coordonnées du fabricantnon disponible
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Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing)
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Gebundene Ausgabe. Etat : Neu. Neu Neuware; teils original eingeschweisst; Rechnung mit MwSt.; new item, still sealed; -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). 288 pp. Englisch. N° de réf. du vendeur INF1100263090
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Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing)
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Hard Cover. Etat : Good. No Jacket. Ex-library with the usual features. The interior is clean and tight. Binding and cover are good. 180 pages. 265 pages. Ex-Library. N° de réf. du vendeur 120489
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Evolutionary Computation in Data Mining
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. State of the art in the area of Data Mining and Knowledge Discovery with Evolutionary AlgorithmsDemonstrates how the different tools of evolutionary computation can be used for solving real-life problems in data mining and bioinformaticsDa. N° de réf. du vendeur 4885527
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Evolutionary Computation in Data Mining
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Springer Berlin Heidelberg, 2004
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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). N° de réf. du vendeur 9783540223702
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Evolutionary Computation in Data Mining
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ISBN 10 : 3540223703
ISBN 13 : 9783540223702
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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). 288 pp. Englisch. N° de réf. du vendeur 9783540223702
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Evolutionary Computation in Data Mining
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ISBN 10 : 3540223703
ISBN 13 : 9783540223702
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Buch. Etat : Neu. Neuware -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).Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 288 pp. Englisch. N° de réf. du vendeur 9783540223702
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Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing, 163)
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