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Predictive Clustering

Buch | Hardcover
240 Seiten
2025 | 1st ed. 2025
Springer-Verlag New York Inc.
978-1-4614-1146-8 (ISBN)
CHF 112,25 inkl. MwSt
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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.

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

Erscheint lt. Verlag 25.4.2025
Zusatzinfo V, 240 p.
Verlagsort New York, NY
Sprache englisch
Maße 155 x 235 mm
Themenwelt Informatik Datenbanken Data Warehouse / Data Mining
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Mathematik / Informatik Mathematik
Schlagworte analysis of remote sensing data • bio-informatics • classification • Clustering • clustering time-series data • Data Mining • eco-informatics • Ecological modeling • environmental quality classification • gene expression data analysis • habitat modeling • hierarchical classification • Learning Algorithms • machine learning • multi-label classification • multi-target learning • multi-task learning • predicting gene function • Predictive clustering • Regression • rule learning • Species distribution • structured output prediction • tree learning
ISBN-10 1-4614-1146-7 / 1461411467
ISBN-13 978-1-4614-1146-8 / 9781461411468
Zustand Neuware
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