Data Mining and Knowledge Discovery Handbook
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Beschreibung
Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository.
This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security.
Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.
Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository.
This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security.
Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.
Most complete, extensive and modern handbook available today in the field of data mining, the core of the knowledge discovery process
Algorithmic descriptions are detailed so the reader can understand exactly how they work, and thus implement, modify and intelligently use them
Includes detailed tutorials, and each topic is supplemented with references for further study
This handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified whole. The book first surveys, then provides comprehensive yet concise algorithmic descriptions of classic methods plus recently-developed extensions and novel methods. The volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level computer science and engineering students. It is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.
Autor*in
Oded Maimon
Themen in »Data Mining and Knowledge Discovery Handbook«
Bayesian networks KAP_D018 KDD KLT KLTcatalog algorithm currentjm data mining data mining applications decision trees ensemble method knowledge discovery large datasets preprocessing method soft computing method
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"… ein breites inhaltliches Spektrum der … Konzepte und Anwendungen des Data Mining und Knowledge Discovery in Data Bases … Trotz … der thematischen Breite gelingt es den Herausgebern durch ein didaktisch schlüssiges Konzept, grundlegende und weiterführende Sachverhalte des Data Mining und des KDD strukturiert und nachvollziehbar zu vermitteln. … Als hilfreich für die Orientierung erweist sich der umfangreiche Index. … Das Buch kann daher jedem, der sich erstmals oder vertiefend mit dem Data Mining und dem KDD in seiner gesamten Breite beschäftigen möchte, empfohlen werden …" (Matthias Meyer, in: OR News, 2007, Issue 31, S. 28)
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