Cantitate/Preț
Produs
Total produse
Vezi coșul
Foundations and Novel Approaches in Data Mining de Tsau Young Lin

Foundations and Novel Approaches in Data Mining: Studies in Computational Intelligence, cartea 9

Editat de Tsau Young Lin, Setsuo Ohsuga, Churn-Jung Liau, Xiaohua Hu
en Limba Engleză Paperback – 20 noi 2010
Data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor" syndrome. Currently, application oriented engineers are only concerned with their immediate problems, which results in an ad hoc method of problem solving. Researchers, on the other hand, lack an understanding of the practical issues of data-mining for real-world problems and often concentrate on issues that are of no significance to the practitioners. In this volume, we hope to remedy problems by (1) presenting a theoretical foundation of data-mining, and (2) providing important new directions for data-mining research. A set of well respected data mining theoreticians were invited to present their views on the fundamental science of data mining. We have also called on researchers with practical data mining experiences to present new important data-mining topics.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 92155 lei  6-8 săpt.
  Springer Berlin, Heidelberg – 20 noi 2010 92155 lei  6-8 săpt.
Hardback (1) 92766 lei  6-8 săpt.
  Springer Berlin, Heidelberg – 3 noi 2005 92766 lei  6-8 săpt.

Din seria Studies in Computational Intelligence

Preț: 92155 lei

Preț vechi: 112385 lei
-18% Nou

Puncte Express: 1382
Preț estimativ în valută:
17636€ • 18494$ • 14705£

Carte tipărită la comandă

Livrare economică 08-22 ianuarie 25

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783642066504
ISBN-10: 364206650X
Pagini: 388
Ilustrații: X, 378 p.
Dimensiuni: 155 x 235 x 20 mm
Greutate: 0.54 kg
Ediția:Softcover reprint of hardcover 1st ed. 2006
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Computational Intelligence

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

From the contents Part I: Theoretical Foundations. Commonsense Causal Modeling in the Data Mining Context. Definability of Association Rules in Predicate Calculus. A Measurement-Theoretic Foundation of Rule Interestingness Evaluation. Statistical Independence as Linear Dependence in a Contingency Table. Foundations of Classification.- Part II: Novel Approaches. SVM-OD: SVM Method to Detect Outliers. Extracting Rules from Incomplete Decision Systems: System ERID. Mining for Patterns Based on Contingency Tables by KL-Miner – First Experience. Knowledge Discovery in Fuzzy Databases Using Attribute-Oriented Induction. Rough Set Strategies to Data with Missing Attribute Values. Privacy-Preserving Collaborative Data Mining.- Part III: Novel Applications. Research Issues in Web Structural Delta Mining. Workflow Reduction for Reachable-path Rediscovery in Workflow Mining. Principal Component-based Anomaly Detection Scheme. Making Better Sense of the Demographic Data Value in the Data Mining Procedure.

Textul de pe ultima copertă

Data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor” syndrome. Currently, application oriented engineers are only concerned with their immediate problems, which results in an ad hoc method of problem solving. Researchers, on the other hand, lack an understanding of the practical issues of data-mining for realworld problems and often concentrate on issues that are of no significance to the practitioners. In this volume, we hope to remedy problems by (1) presenting a theoretical foundation of data-mining, and (2) providing important new directions for data-mining research. A set of well respected data mining theoreticians were invited to present their views on the fundamental science of data mining. We have also called on researchers with practical data mining experiences to present new important data-mining topics.

Caracteristici

Latest results in data mining Includes supplementary material: sn.pub/extras