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On This Book: Clustering, Multisets, Rough Sets and Fuzzy Sets Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Experiences Using Decision Trees for Knowledge Discovery

Authors : Eva Armengol, Àngel García-Cerdaña, Pilar Dellunde

Published in: Fuzzy Sets, Rough Sets, Multisets and Clustering

Publisher: Springer International Publishing

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Abstract

Knowledge discovery is the process of identifying useful patterns from large data sets. There are two families of approaches to be used for knowledge discovery: clustering, when the classes of domain objects are not known; and inductive learning algorithms, when the classes are known and the goal is to construct a domain model useful to identify new unseen objects. Clustering algorithms have also been proposed to analyze the data when the classes are known. However, to our knowledge, inductive learning methods are not used to analyze the available data but only for prediction. What we propose here is a methodology, namely FTree, that uses a decision tree to analyze both the available data identifying patterns and some important aspects of the domain (at least from the domain’s part represented by the data at hand) such as similarity between classes, separability, characterization of classes and even some possible errors on data.

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Metadata
Title
Experiences Using Decision Trees for Knowledge Discovery
Authors
Eva Armengol
Àngel García-Cerdaña
Pilar Dellunde
Copyright Year
2017
Book
Fuzzy Sets, Rough Sets, Multisets and Clustering

Print ISBN: 978-3-319-47556-1

Electronic ISBN: 978-3-319-47557-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-47557-8_11

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