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Advanced Data Mining Techniques [electronic resource] /by David L. Olson, Dursun Delen.

by Olson, David L [author.]; Delen, Dursun [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.Description: online resource.ISBN: 9783540769170.Subject(s): Economics | Data mining | Management information systems | Economics/Management Science | Business Information Systems | Operations Research/Decision Theory | Data Mining and Knowledge DiscoveryDDC classification: 650 Online resources: Click here to access online
Contents:
Data Mining Process -- Data Mining Methods As Tools -- Memory-Based Reasoning Methods -- Association Rules in Knowledge Discovery -- Fuzzy Sets in Data Mining -- Rough Sets -- Support Vector Machines -- Genetic Algorithm Support to Data Mining -- Performance Evaluation for Predictive Modeling -- Applications -- Applications of Methods.
In: Springer eBooksSummary: This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focusses on business applications of data mining. Methods are presented with simple examples, applications are reviewed, and relativ advantages are evaluated.
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Data Mining Process -- Data Mining Methods As Tools -- Memory-Based Reasoning Methods -- Association Rules in Knowledge Discovery -- Fuzzy Sets in Data Mining -- Rough Sets -- Support Vector Machines -- Genetic Algorithm Support to Data Mining -- Performance Evaluation for Predictive Modeling -- Applications -- Applications of Methods.

This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focusses on business applications of data mining. Methods are presented with simple examples, applications are reviewed, and relativ advantages are evaluated.

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