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Sinopsis
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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De la contraportada
This carefully edited book reflects and advances the state of the art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms. It emphasizes the utility of different evolutionary computing tools to various facets of knowledge discovery from databases, ranging from theoretical analysis to real-life applications. "Evolutionary Computation in Data Mining" provides a balanced mixture of theory, algorithms and applications in a cohesive manner, and demonstrates how the different tools of evolutionary computation can be used for solving real-life problems in data mining and bioinformatics.
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- EditorialSpringer
- Año de publicación2004
- ISBN 10 3540223703
- ISBN 13 9783540223702
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de páginas288
- EditorGhosh Ashish
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Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing)
Librería: Versandbuchhandlung Kisch & Co., Fürstenberg OT Blumenow, Alemania
Calificación del vendedor: 5 de 5 estrellas
Gebundene Ausgabe. Condición: 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. Nº de ref. del artículo: INF1100263090
Cantidad disponible: 1 disponibles
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Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing)
Librería: BookOrders, Russell, IA, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Hard Cover. Condición: 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. Nº de ref. del artículo: 120489
Cantidad disponible: 1 disponibles
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Evolutionary Computation in Data Mining
Publicado por
Springer Berlin Heidelberg, 2004
ISBN 10: 3540223703
ISBN 13: 9783540223702
Nuevo
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Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Gebunden. Condición: 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. Nº de ref. del artículo: 4885527
Cantidad disponible: Más de 20 disponibles
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Evolutionary Computation in Data Mining
Publicado por
Springer Berlin Heidelberg Okt 2004, 2004
ISBN 10: 3540223703
ISBN 13: 9783540223702
Nuevo
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: 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. Nº de ref. del artículo: 9783540223702
Cantidad disponible: 2 disponibles
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Evolutionary Computation in Data Mining
Publicado por
Springer Berlin Heidelberg, 2004
ISBN 10: 3540223703
ISBN 13: 9783540223702
Nuevo
Tapa dura
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: 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). Nº de ref. del artículo: 9783540223702
Cantidad disponible: 1 disponibles
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Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing, 163)
Librería: BennettBooksLtd, North Las Vegas, NV, Estados Unidos de America
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hardcover. Condición: New. In shrink wrap. Looks like an interesting title! Nº de ref. del artículo: Q-3540223703
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Evolutionary Computation In Data Mining
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Condición: New. Nº de ref. del artículo: 3318572-n
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Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing, 163)
Librería: California Books, Miami, FL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: I-9783540223702
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Evolutionary Computation in Data Mining
Publicado por
Springer Berlin Heidelberg, Springer Berlin Heidelberg Okt 2004, 2004
ISBN 10: 3540223703
ISBN 13: 9783540223702
Nuevo
Tapa dura
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: 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. Nº de ref. del artículo: 9783540223702
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Evolutionary Computation In Data Mining
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Condición: New. Nº de ref. del artículo: 3318572-n
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