Principles of Data Mining and Knowledge Discovery (häftad)
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Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
701
Utgivningsdatum
2000-09-01
Upplaga
2000 ed.
Förlag
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Medarbetare
Zighed, Djamel A. (ed.), Komorowski, Jan (ed.), Zytkow, Jan (ed.)
Illustratör/Fotograf
Zahlr Abb und Tab
Illustrationer
XV, 701 p.
Dimensioner
234 x 156 x 37 mm
Vikt
999 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783540410669

Principles of Data Mining and Knowledge Discovery

4th European Conference, PKDD, 2000, Lyon, France, September 13-16, 2000 Proceedings

av Djamel A Zighed, Jan Komorowski, Jan Zytkow
Häftad,  Engelska, 2000-09-01
1505
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This volume contains papers selected for presentation at PKDD'2000, the Fourth European Conference on Principles and Practice of Knowledge Discovery in - tabases. The rst meeting was held in Trondheim, Norway, in June 1997, the second in Nantes, France, in September 1998, and the third in Prague, Czech Republic, in September 1999. PKDD 2000 was organized in Lyon, France, on 13{16 September 2000. The conference was hosted by the Equipe de Recherche en Ing enierie des Conna- sances at the Universit e Lumi ere Lyon 2. We wish to express our thanks to the sponsors of the Conference, to the University Claude Bernard Lyon 1, the INSA of Lyon, the Conseil g en eral of the R^one, the R egion Rh^one Alpes, SPSS France, AFIA, and the University of Lyon 2, for their generous support. Knowledge discovery in databases (KDD), also known as data mining, p- vides tools for turning large databases into knowledge that can be used in pr- tice. KDD has been able to grow very rapidly since its emergence a decade ago by drawing its techniques and data mining experiences from a combination of many existing research areas: databases, statistics, mathematical logic, machine learning, automated scienti c discovery, inductive logic programming, arti cial intelligence, visualization, decision science, knowledge management, and high performance computing. The strength of KDD came initially from the value - ded by the creative combination of techniques from the contributing areas.
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Djamel A Zighed, Jan Komorowski, Jan Zytkow

Innehållsförteckning

Towards Broader Foundations.- Rules and Trees.- Databases and Reward-Based Learning.- Classication.- Association Rules and Exceptions.- Instance-Based Discovery.- Clustering and Classification.- Time Series.- Posters.

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