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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
This book introduces a novel paradigm for machine learning and data mining called predictive clustering, which covers a broad variety of learning tasks and offers a fresh perspective on existing techniques.
The book presents an informal introduction to predictive clustering, describing learning tasks and settings, and then continues with a formal description of the paradigm, explaining algorithms for learning predictive clustering trees and predictive clustering rules, as well as presenting the applicability of these learning techniques to a broad range of tasks. Variants of decision tree learning algorithms are also introduced. Finally, the book offers several significant applications in ecology and bio-informatics.
The book is written in a straightforward and easy-to-understand manner, aimed at varied readership, ranging from researchers with an interest in machine learning techniques to practitioners of data mining technology in the areas of ecology and bioinformatics.
Content Level »Professional/practitioner
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
Introduction.- What is predictive clustering?.- Motivation: A variety of predictive learning tasks.- Some basic approaches to prediction and clustering.- Formalizing predictive clustering.- Predictive clustering trees.- Predictive clustering rules.- Distances and prototype functions.- Predictive Clustering with Constraints.- Relational PCTs.- Applications in environmental sciences.- Applications in bioinformatics.- Clus