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Sinopsis
Covers the key topics of assessing the quality of the results and handling uncertainty in data mining.
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Reseña del editor
The recent explosive growth of our ability to generate and store data has created a need for new, scalable and efficient, tools for data analysis. The main focus of the discipline of knowledge discovery in databases is to address this need. Knowledge discovery in databases is the fusion of many areas that are concerned with different aspects of data handling and data analysis, including databases, machine learning, statistics, and algorithms. Each of these areas addresses a different part of the problem, and places different emphasis on different requirements. For example, database techniques are designed to efficiently handle relatively simple queries on large amounts of data stored in external (disk) storage. Machine learning techniques typically consider smaller data sets, and the emphasis is on the accuracy ofa relatively complicated analysis task such as classification. The analysis of large data sets requires the design of new tools that not only combine and generalize techniques from different areas, but also require the design and development ofaltogether new scalable techniques.
Reseña del editor
Uncertainty Handling and Quality Assessment in Data Mining provides an introduction to the application of these concepts in Knowledge Discovery and Data Mining. It reviews the state-of-the-art in uncertainty handling and discusses a framework for unveiling and handling uncertainty. Coverage of quality assessment begins with an introduction to cluster analysis and a comparison of the methods and approaches that may be used. The techniques and algorithms involved in other essential data mining tasks, such as classification and extraction of association rules, are also discussed together with a review of the quality criteria and techniques for evaluating the data mining results. This book presents a general framework for assessing quality and handling uncertainty which is based on tested concepts and theories. This framework forms the basis of an implementation tool, 'Uminer' which is introduced to the reader for the first time. This tool supports the key data mining tasks while enhancing the traditional processes for handling uncertainty and assessing quality. Aimed at IT professionals involved with data mining and knowledge discovery, the work is supported with case studies from epidemiology and telecommunications that illustrate how the tool works in 'real world' data mining projects. The book would also be of interest to final year undergraduates or post-graduate students looking at: databases, algorithms, artificial intelligence and information systems particularly with regard to uncertainty and quality assessment.
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Uncertainty Handling and Quality Assessment in Data Mining (Advanced Information and Knowledge Processing)
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Uncertainty Handling and Quality Assessment in Data Mining
Impresión bajo demandaLibrería: moluna, Greven, Alemania
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Focuses on the quality assessment of the results and the use of uncertainty in data mining rather than providing a general treatment of the subject of data miningThe recent explosive growth of our ability to generate and store data has created a need fo. Nº de ref. del artículo: 4184002
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Uncertainty Handling and Quality Assessment in Data Mining
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The recent explosive growth of our ability to generate and store data has created a need for new, scalable and efficient, tools for data analysis. The main focus of the discipline of knowledge discovery in databases is to address this need. Knowledge discovery in databases is the fusion of many areas that are concerned with different aspects of data handling and data analysis, including databases, machine learning, statistics, and algorithms. Each of these areas addresses a different part of the problem, and places different emphasis on different requirements. For example, database techniques are designed to efficiently handle relatively simple queries on large amounts of data stored in external (disk) storage. Machine learning techniques typically consider smaller data sets, and the emphasis is on the accuracy ofa relatively complicated analysis task such as classification. The analysis of large data sets requires the design of new tools that not only combine and generalize techniques from different areas, but also require the design and development ofaltogether new scalable techniques. Nº de ref. del artículo: 9781447111191
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Uncertainty Handling and Quality Assessment in Data Mining
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PF. Condición: New. Nº de ref. del artículo: 6666-IUK-9781447111191
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Uncertainty Handling and Quality Assessment in Data Mining
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Springer London Ltd, 2012
ISBN 10: 1447111192
ISBN 13: 9781447111191
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Uncertainty Handling and Quality Assessment in Data Mining
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Condición: New. pp. 240. Nº de ref. del artículo: 2654523097
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Uncertainty Handling and Quality Assessment in Data Mining
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Springer London, Springer London Okt 2012, 2012
ISBN 10: 1447111192
ISBN 13: 9781447111191
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The recent explosive growth of our ability to generate and store data has created a need for new, scalable and efficient, tools for data analysis. The main focus of the discipline of knowledge discovery in databases is to address this need. Knowledge discovery in databases is the fusion of many areas that are concerned with different aspects of data handling and data analysis, including databases, machine learning, statistics, and algorithms. Each of these areas addresses a different part of the problem, and places different emphasis on different requirements. For example, database techniques are designed to efficiently handle relatively simple queries on large amounts of data stored in external (disk) storage. Machine learning techniques typically consider smaller data sets, and the emphasis is on the accuracy ofa relatively complicated analysis task such as classification. The analysis of large data sets requires the design of new tools that not only combine and generalize techniques from different areas, but also require the design and development ofaltogether new scalable techniques.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 240 pp. Englisch. Nº de ref. del artículo: 9781447111191
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Uncertainty Handling and Quality Assessment in Data Mining
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Condición: New. Print on Demand pp. 240 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam. Nº de ref. del artículo: 55069446
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Uncertainty Handling and Quality Assessment in Data Mining
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UNCERTAINTY HANDLING AND QUALITY ASSESSMENT IN DATA MINING (ADVANCED INFORMATION AND KNOWLEDGE PROCESSING)
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Condición: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Nº de ref. del artículo: ABEJUNE24-178205
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