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Erschienen in: Artificial Intelligence Review 2/2019

23.03.2018

A review of conceptual clustering algorithms

verfasst von: Airel Pérez-Suárez, José F. Martínez-Trinidad, Jesús A. Carrasco-Ochoa

Erschienen in: Artificial Intelligence Review | Ausgabe 2/2019

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Abstract

Clustering is a fundamental technique in data mining and pattern recognition, which has been successfully applied in several contexts. However, most of the clustering algorithms developed so far have been focused only in organizing the collection of objects into a set of clusters, leaving the interpretation of those clusters to the user. Conceptual clustering algorithms, in addition to the list of objects belonging to the clusters, provide for each cluster one or several concepts, as an explanation of the clusters. In this work, we present an overview of the most influential algorithms reported in the field of conceptual clustering, highlighting their limitations or drawbacks. Additionally, we present a taxonomy of these methods as well as a qualitative comparison of these algorithms, regarding a set of characteristics desirable since a practical point of view, which may help in the selection of the most appropriate method for solving a problem at hand. Finally, some research lines that need to be further developed in the context of conceptual clustering are discussed.

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Metadaten
Titel
A review of conceptual clustering algorithms
verfasst von
Airel Pérez-Suárez
José F. Martínez-Trinidad
Jesús A. Carrasco-Ochoa
Publikationsdatum
23.03.2018
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence Review / Ausgabe 2/2019
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-018-9627-1

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