Hierarchical Clustering of Composite Objects with a Variable Number of Components

Alain Ketterlin, Pierre Gançarski, Jerzy J. Korczak
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:303-309, 1995.

Abstract

This paper examines the problem of clustering a sequence of objects that cannot be described with a predefined list of attributes (or variables). In many applications, such a crisp representation cannot be determined. An extension of the traditionnal propositionnal formalism is thus proposed, which allows objects to be represented as a set of components. The algorithm used for clustering is briefly illustrated, and mechanisms to handle sets are described. Some empirical evaluations are also provided, to assess the validity of the approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-ketterlin95a, title = {Hierarchical Clustering of Composite Objects with a Variable Number of Components}, author = {Ketterlin, Alain and Gan{\c{c}}arski, Pierre and Korczak, Jerzy J.}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {303--309}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/ketterlin95a/ketterlin95a.pdf}, url = {https://proceedings.mlr.press/r0/ketterlin95a.html}, abstract = {This paper examines the problem of clustering a sequence of objects that cannot be described with a predefined list of attributes (or variables). In many applications, such a crisp representation cannot be determined. An extension of the traditionnal propositionnal formalism is thus proposed, which allows objects to be represented as a set of components. The algorithm used for clustering is briefly illustrated, and mechanisms to handle sets are described. Some empirical evaluations are also provided, to assess the validity of the approach.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Hierarchical Clustering of Composite Objects with a Variable Number of Components %A Alain Ketterlin %A Pierre Gançarski %A Jerzy J. Korczak %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-ketterlin95a %I PMLR %P 303--309 %U https://proceedings.mlr.press/r0/ketterlin95a.html %V R0 %X This paper examines the problem of clustering a sequence of objects that cannot be described with a predefined list of attributes (or variables). In many applications, such a crisp representation cannot be determined. An extension of the traditionnal propositionnal formalism is thus proposed, which allows objects to be represented as a set of components. The algorithm used for clustering is briefly illustrated, and mechanisms to handle sets are described. Some empirical evaluations are also provided, to assess the validity of the approach. %Z Reissued by PMLR on 01 May 2022.
APA
Ketterlin, A., Gançarski, P. & Korczak, J.J.. (1995). Hierarchical Clustering of Composite Objects with a Variable Number of Components. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:303-309 Available from https://proceedings.mlr.press/r0/ketterlin95a.html. Reissued by PMLR on 01 May 2022.

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