Clustering with decision trees: Divisive and agglomerative approach

Lauriane Castin; Benoit Frénay

Clustering with decision trees: Divisive and agglomerative approach

Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publisher	i6doc.com publication
Pages	455-460
Number of pages	6
ISBN (Electronic)	9782875870476
ISBN (Print)	9782875870476
Publication status	Published - 1 Jan 2018
Event	26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018 - Bruges, Belgium Duration: 25 Apr 2018 → 27 Apr 2018

Publication series

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

Conference

Conference	26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018
Country/Territory	Belgium
City	Bruges
Period	25/04/18 → 27/04/18

Cite this

Castin, L., & Frénay, B. (2018). Clustering with decision trees: Divisive and agglomerative approach. In ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 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. pp. 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,

day = "1",

language = "English",

isbn = "9782875870476",

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

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pages = "455--460",

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

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Castin, L & Frénay, B 2018, Clustering with decision trees: Divisive and agglomerative approach. in 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, pp. 455-460, 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018, Bruges, Belgium, 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).

Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution

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

T3 - ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

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. In 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

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