9783540223702 - evolutionary computation in data mining (studies in fuzziness and soft computing, 163, band 163) (15 Ergebnisse)

Sprache: Englisch
Verlag: Springer, 2004
Serie: Buch 13 von 183 - Studies in Fuzziness and Soft Computing
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Gebundene Ausgabe. Zustand: 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.

Sprache: Englisch
Verlag: Springer, 2004
Serie: Buch 13 von 183 - Studies in Fuzziness and Soft Computing
- Hardcover
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Gebundene Ausgabe. Zustand: 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.

Sprache: Englisch
Verlag: Springer, 2004
Serie: Buch 13 von 183 - Studies in Fuzziness and Soft Computing
- Hardcover
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hardcover. Zustand: New. In shrink wrap. Looks like an interesting title.

Sprache: Englisch
Verlag: Springer, 2004
Serie: Buch 13 von 183 - Studies in Fuzziness and Soft Computing
- Hardcover
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Sprache: Englisch
Verlag: Springer, 2004
Serie: Buch 13 von 183 - Studies in Fuzziness and Soft Computing
- Hardcover
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Sprache: Englisch
Verlag: Springer, 2004
Serie: Buch 13 von 183 - Studies in Fuzziness and Soft Computing
- Hardcover
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Zustand: New. pp. 288.

Sprache: Englisch
Verlag: Springer Berlin Heidelberg, Springer Berlin Heidelberg, 2004
Serie: Buch 13 von 183 - Studies in Fuzziness and Soft Computing
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Buch. Zustand: 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 extra…cts 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).

Sprache: Englisch
Verlag: Springer, 2004
Serie: Buch 13 von 183 - Studies in Fuzziness and Soft Computing
- Hardcover
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Sprache: Englisch
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Serie: Buch 13 von 183 - Studies in Fuzziness and Soft Computing
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Zustand: gut. 2005. Evolutionary computation in data mining. Studies in fuzziness and soft computing ; Vol. 163 In deutscher Sprache. pages.

Sprache: Englisch
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Buch. Zustand: 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, whic…h 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.

Sprache: Englisch
Verlag: Springer Berlin Heidelberg, 2004
Serie: Buch 13 von 183 - Studies in Fuzziness and Soft Computing
- Hardcover
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Gebunden. Zustand: 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 da…ta mining and bioinformaticsDa.

Sprache: Englisch
Verlag: Springer, 2004
Serie: Buch 13 von 183 - Studies in Fuzziness and Soft Computing
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Zustand: New. Print on Demand pp. 288 Illus.

Sprache: Englisch
Verlag: Springer, 2004
Serie: Buch 13 von 183 - Studies in Fuzziness and Soft Computing
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Zustand: New. PRINT ON DEMAND pp. 288.

Sprache: Englisch
Verlag: Springer, 2004
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Buch. Zustand: Neu. Evolutionary Computation in Data Mining | Ashish Ghosh | Buch | Studies in Fuzziness and Soft Computing | xviii | Englisch | 2004 | Springer | EAN 9783540223702 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter:…preigu Print on Demand.

Sprache: Englisch
Verlag: Springer, Springer Okt 2004, 2004
Serie: Buch 13 von 183 - Studies in Fuzziness and Soft Computing
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Buch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. 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 ac…tually 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 KG, Sachsenplatz 4-6, 1201 Wien 288 pp. Englisch.