Advanced methods knowledge discovery (38 résultats)

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Hardback. Etat : New. 2005 ed. Knowledge discovery takes the raw results from data mining (the process of extracting trends or patterns from data) and carefully and accurately transforms them into useful and understandable information. In this book, active practitioners and leading researchers detail recent advances in knowledge discovery. Coverage presents a good balance of introductory material on the knowledge discovery process, advanced issues, and state-of-the-art tools and techniques. An overview of the field, looking at the issues and challenges involved, is followed by coverage of recent trends and important applications of advanced data mining techniques in areas such as life sciences, world-wide web, image databases, cyber security, and sensor networks.…

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti c and engineering research and the development of e cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the followingchapters.…

Taschenbuch. Etat : Neu. Advanced Methods for Knowledge Discovery from Complex Data | Ujjwal Maulik (u. a.) | Taschenbuch | Advanced Information and Knowledge Processing | xviii | Englisch | 2010 | Springer | EAN 9781849969918 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

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Hardback. Etat : New. 2005 ed. Knowledge discovery takes the raw results from data mining (the process of extracting trends or patterns from data) and carefully and accurately transforms them into useful and understandable information. In this book, active practitioners and leading researchers detail recent advances in knowledge discovery. Coverage presents a good balance of introductory material on the knowledge discovery process, advanced issues, and state-of-the-art tools and techniques. An overview of the field, looking at the issues and challenges involved, is followed by coverage of recent trends and important applications of advanced data mining techniques in areas such as life sciences, world-wide web, image databases, cyber security, and sensor networks.…

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti c and engineering research and the development of e cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters. 388 pp. Englisch.…