Clustering with decision trees: Divisive and agglomerative approach

Lauriane Castin; Benoit Frénay

Clustering with decision trees: Divisive and agglomerative approach

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

Résumé

Decision trees are mainly used to perform classification tasks. Samples are submitted to a test in each node of the tree and guided through the tree based on the result. Decision trees can also be used to perform clustering, with a few adjustments. On one hand, new split criteria must be discovered to construct the tree without the knowledge of samples labels. On the other hand, new algorithms must be applied to merge sub-clusters at leaf nodes into actual clusters. In this paper, new split criteria and agglomeration algorithms are developed for clustering, with results comparable to other existing clustering techniques.

langue originale	Anglais
titre	ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Editeur	i6doc.com publication
Pages	455-460
Nombre de pages	6
ISBN (Electronique)	9782875870476
ISBN (imprimé)	9782875870476
Etat de la publication	Publié - 1 janv. 2018
Evénement	26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018 - Bruges, Belgique Durée: 25 avr. 2018 → 27 avr. 2018

Série de publications

Nom	ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Une conférence

Une conférence	26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018
Pays/Territoire	Belgique
La ville	Bruges
période	25/04/18 → 27/04/18

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Contient cette citation

Castin, L., & Frénay, B. (2018). Clustering with decision trees: Divisive and agglomerative approach. Dans ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (p. 455-460). (ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning). i6doc.com publication.

Castin, Lauriane ; Frénay, Benoit. / Clustering with decision trees : Divisive and agglomerative approach. ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2018. p. 455-460 (ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

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title = "Clustering with decision trees: Divisive and agglomerative approach",

abstract = "Decision trees are mainly used to perform classification tasks. Samples are submitted to a test in each node of the tree and guided through the tree based on the result. Decision trees can also be used to perform clustering, with a few adjustments. On one hand, new split criteria must be discovered to construct the tree without the knowledge of samples labels. On the other hand, new algorithms must be applied to merge sub-clusters at leaf nodes into actual clusters. In this paper, new split criteria and agglomeration algorithms are developed for clustering, with results comparable to other existing clustering techniques.",

author = "Lauriane Castin and Benoit Fr{\'e}nay",

note = "Publisher Copyright: {\textcopyright} ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018 ; Conference date: 25-04-2018 Through 27-04-2018",

year = "2018",

month = jan,

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Castin, L & Frénay, B 2018, Clustering with decision trees: Divisive and agglomerative approach. Dans ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, i6doc.com publication, p. 455-460, 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018, Bruges, Belgique, 25/04/18.

Clustering with decision trees: Divisive and agglomerative approach. / Castin, Lauriane; Frénay, Benoit.
ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2018. p. 455-460 (ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

TY - GEN

T1 - Clustering with decision trees

T2 - 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018

AU - Castin, Lauriane

AU - Frénay, Benoit

N1 - Publisher Copyright: © ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Decision trees are mainly used to perform classification tasks. Samples are submitted to a test in each node of the tree and guided through the tree based on the result. Decision trees can also be used to perform clustering, with a few adjustments. On one hand, new split criteria must be discovered to construct the tree without the knowledge of samples labels. On the other hand, new algorithms must be applied to merge sub-clusters at leaf nodes into actual clusters. In this paper, new split criteria and agglomeration algorithms are developed for clustering, with results comparable to other existing clustering techniques.

AB - Decision trees are mainly used to perform classification tasks. Samples are submitted to a test in each node of the tree and guided through the tree based on the result. Decision trees can also be used to perform clustering, with a few adjustments. On one hand, new split criteria must be discovered to construct the tree without the knowledge of samples labels. On the other hand, new algorithms must be applied to merge sub-clusters at leaf nodes into actual clusters. In this paper, new split criteria and agglomeration algorithms are developed for clustering, with results comparable to other existing clustering techniques.

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M3 - Conference contribution

SN - 9782875870476

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SP - 455

EP - 460

BT - ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

PB - i6doc.com publication

Y2 - 25 April 2018 through 27 April 2018

ER -

Castin L, Frénay B. Clustering with decision trees: Divisive and agglomerative approach. Dans ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication. 2018. p. 455-460. (ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Clustering with decision trees: Divisive and agglomerative approach

Résumé

Série de publications

Une conférence

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Empreinte digitale

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