Principles of Data Mining and Knowledge Discovery: 4th European Conference, PKDD, 2000, Lyon, France, September 13-16, 2000 Proceedings
Author:   Djamel A. Zighed ,  Jan Komorowski ,  Jan Zytkow
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Edition:   2000 ed.
Volume:   1910
ISBN:  

9783540410669


Pages:   701
Publication Date:   06 September 2000
Format:   Paperback
Availability:   In Print   Availability explained
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Principles of Data Mining and Knowledge Discovery: 4th European Conference, PKDD, 2000, Lyon, France, September 13-16, 2000 Proceedings


Overview

This book constitutes the refereed proceedings of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2000, held in Lyon, France in September 2000. The 86 revised papers included in the book correspond to the 29 oral presentations and 57 posters presented at the conference. They were carefully reviewed and selected from 147 submissions. The book offers topical sections on new directions, rules and trees, databases and reward-based learning, classification, association rules and exceptions, instance-based discovery, clustering, and time series analysis.

Full Product Details

Author:   Djamel A. Zighed ,  Jan Komorowski ,  Jan Zytkow
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   2000 ed.
Volume:   1910
Dimensions:   Width: 15.50cm , Height: 3.60cm , Length: 23.30cm
Weight:   2.200kg
ISBN:  

9783540410669


ISBN 10:   354041066
Pages:   701
Publication Date:   06 September 2000
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

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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