Authors:
- Features a new data mining approach: predictive clustering trees and rules
- Presents a higher efficiency of the learning and prediction process
- Provides straightforward content in an easy-to-understand manner
About this book
Keywords
- Data mining
- Machine learning
- Predictive clustering
- analysis of remote sensing data
- bio-informatics
- classification
- clustering
- clustering time-series data
- eco-informatics
- ecological modeling
- environmental quality classification
- gene expression data analysis
- habitat modeling
- hierarchical classification
- learning algorithms
- multi-label classification
- multi-target learning
- multi-task learning
- predicting gene function
- regression
- rule learning
- species distribution
- structured output prediction
- tree learning
Authors and Affiliations
-
Katholieke Universiteit Leuven, Heverlee, Belgium
Hendrik Blockeel
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Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia
Sašo Džeroski
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Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium
Jan Struyf
-
Jožef Stefan Institute, Ljubljana, Slovenia
Bernard Zenko
Bibliographic Information
Book Title: Predictive Clustering
Authors: Hendrik Blockeel, Sašo Džeroski, Jan Struyf, Bernard Zenko
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Science+Business Media, LLC, part of Springer Nature 2025
Hardcover ISBN: 978-1-4614-1146-8
eBook ISBN: 978-1-4614-1147-5
Edition Number: 1
Number of Pages: V, 240