Wesley Chu / Tsau Young Lin (eds.)

Foundations and Advances in Data Mining

Herausgegeben:Chu, Wesley; Lin, Tsau Young

• Gebundenes Buch

Produktbeschreibung

With the growing use of information technology and the recent advances in web systems, the amount of data available to users has increased exponentially. Thus, there is a critical need to understand the content of the data. As a result, data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor" syndrome. In this carefully edited volume a theoretical foundation as well as important new directions for data-mining research are presented. It brings together a set of well respected data mining theoreticians and researchers with practical data mining experiences. The presented theories will give data mining practitioners a scientific perspective in data mining and thus provide more insight into their problems, and the provided new data mining topics can be expected to stimulate further research in these important directions.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Studies in Fuzziness and Soft Computing 180
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 11362197, 978-3-540-25057-9
• 2005
• Seitenzahl: 352
• Erscheinungstermin: 15. September 2005
• Englisch
• Abmessung: 241mm x 160mm x 24mm
• Gewicht: 646g
• ISBN-13: 9783540250579
• ISBN-10: 3540250573
• Artikelnr.: 13913503

Autorenporträt

Wesley Chu, University of California, Los Angeles, CA, USA / Tsau Young Lin, San Jose State University, San Jose, CA, USA

Inhaltsangabe

The Mathematics of Learning.- Logical Regression Analysis: From Mathematical Formulas to Linguistic Rules.- A Feature/Attribute Theory for Association Mining and Constructing the Complete Feature Set.- A New Theoretical Framework for K-means-type Clustering.- Clustering via Decision Tree Construction.- Incremental Mining on Association Rules.- Mining Association Rules from Tabular Data Guided by Maximal Frequent Itemsets.- Sequential Pattern Mining by Pattern-Growth: Principles and Extensions.- Web Page Classification.- Web Mining - Concepts, Applications, and Research Directions.- Privacy-Preserving Data Mining.