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ISBN: 9781441912800
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2008, ISBN: 9781441912800
Over the course of the last twenty years, research in data mining has seen a substantial increase in interest, attracting original contributions from various disciplines including compute… Mehr…
2008, ISBN: 9781441912800
Over the course of the last twenty years, research in data mining has seen a substantial increase in interest, attracting original contributions from various disciplines including compute… Mehr…
2008
ISBN: 9781441912800
Over the course of the last twenty years, research in data mining has seen a substantial increase in interest, attracting original contributions from various disciplines including compute… Mehr…
ISBN: 9781441912800
Data Mining ab 170.99 € als pdf eBook: Special Issue in Annals of Information Systems. Aus dem Bereich: eBooks, Sachthemen & Ratgeber, Technik, Medien > Bücher nein eBook als pdf, Springe… Mehr…
2009, ISBN: 9781441912800
Special Issue in Annals of Information Systems, eBooks, eBook Download (PDF), 2010, [PU: Springer US], Springer US, 2009
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Detailangaben zum Buch - Data Mining
EAN (ISBN-13): 9781441912800
ISBN (ISBN-10): 1441912800
Erscheinungsjahr: 2009
Herausgeber: Springer-Verlag GmbH
387 Seiten
Sprache: eng/Englisch
Buch in der Datenbank seit 2010-07-01T12:33:44+02:00 (Vienna)
Detailseite zuletzt geändert am 2024-01-26T15:32:37+01:00 (Vienna)
ISBN/EAN: 9781441912800
ISBN - alternative Schreibweisen:
1-4419-1280-0, 978-1-4419-1280-0
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: crone, lessmann, stefan
Titel des Buches: data mining
Daten vom Verlag:
Autor/in: Robert Stahlbock; Sven F. Crone; Stefan Lessmann
Titel: Annals of Information Systems; Data Mining - Special Issue in Annals of Information Systems
Verlag: Springer; Springer US
387 Seiten
Erscheinungsjahr: 2009-11-10
New York; NY; US
Sprache: Englisch
149,79 € (DE)
154,00 € (AT)
177,00 CHF (CH)
Available
XIII, 387 p.
EA; E107; eBook; Nonbooks, PBS / Informatik, EDV/Informatik; Data Mining; Verstehen; Business Intelligence; Text Mining; classification; data analysis; data mining; knowledge discovery; statistics; Engineering Economics; B; Data Mining and Knowledge Discovery; Operations Research and Decision Theory; Business and Management; Computer and Information Systems Applications; Industrial Management; Statistics in Business, Management, Economics, Finance, Insurance; Computer Science; Wissensbasierte Systeme, Expertensysteme; Unternehmensforschung; Management: Entscheidungstheorie; Betriebswirtschaft und Management; Angewandte Informatik; Ingenieurswesen, Maschinenbau allgemein; Management und Managementtechniken; Wahrscheinlichkeitsrechnung und Statistik; Wirtschaftswissenschaft, Finanzen, Betriebswirtschaft und Management; BC
Data Mining and Information Systems: Quo Vadis?.- Confirmatory data analysis.- Response-Based Segmentation Using Finite Mixture Partial Least Squares.- Knowledge discovery from supervised learning.- Building Acceptable Classification Models.- Mining Interesting Rules Without Support Requirement: A General Universal Existential Upward Closure Property.- Classification Techniques and Error Control in Logic Mining.- Classification analysis.- An Extended Study of the Discriminant Random Forest.- Prediction with the SVM Using Test Point Margins.- Effects of Oversampling Versus Cost-Sensitive Learning for Bayesian and SVM Classifiers.- The Impact of Small Disjuncts on Classifier Learning.- Hybrid data mining procedures.- Predicting Customer Loyalty Labels in a Large Retail Database: A Case Study in Chile.- PCA-based Time Series Similarity Search.- Evolutionary Optimization of Least-Squares Support Vector Machines.- Genetically Evolved kNN Ensembles.- Web-mining.- Behaviorally Founded Recommendation Algorithm for Browsing Assistance Systems.- Using Web Text Mining to Predict Future Events: A Test of the Wisdom of Crowds Hypothesis.- Privacy-preserving data mining.- Avoiding Attribute Disclosure with the (Extended) p-Sensitive k-Anonymity Model.- Privacy-Preserving Random Kernel Classification of Checkerboard Partitioned Data.Provides a state-of-art, up-to-the moment, view of the evolving field of datamining Special publication of the 2007 International Conference on Data Mining (DMIN 07) Features a combination of rigorously peer-reviewed theoretical research papers, as well as industrial reports, and case studies on applications Includes supplementary material: sn.pub/extras
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