On joint dimension reduction and clustering of categorical data

Alfonso Iodice D’Enza; Michel Van de Velden; Francesco Palumbo

doi:10.1007/978-3-319-06692-9_18

On joint dimension reduction and clustering of categorical data

Alfonso Iodice D’Enza^*, Michel Van de Velden, Francesco Palumbo

^*Corresponding author for this work

Econometrics

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

3 Citations (Scopus)

Abstract

There exist several methods for clustering high-dimensional data. One popular approach is to use a two-step procedure. In the first step, a dimension reduction technique is used to reduce the dimensionality of the data. In the second step, cluster analysis is applied to the data in the reduced space. This method may be referred to as the tandem approach. An important drawback of this method is that the dimension reduction may distort or hide the cluster structure. As an alternative, various authors have proposed joint dimension reduction and clustering approaches. In this paper we review some of these existing joint dimension reduction and clustering methods for categorical data in a unified framework that facilitates comparison.

Original language	English
Title of host publication	Analysis and Modeling of Complex Data in Behavioral and Social Sciences
Editors	Akinori Okada, Claus Weihs, Donatella Vicari, Giancarlo Ragozini
Pages	161-169
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-319-06692-9_18
Publication status	Published - 2014
Event	Joint international meeting on Japanese Classification Society and the Classification and Data Analysis Group of the Italian Statistical Society, JCS-CLADAG 2012 - Capri Island, Italy Duration: 3 Sept 2012 → 4 Sept 2012

Publication series

Series	Studies in Classification, Data Analysis, and Knowledge Organization
Volume	49
ISSN	1431-8814

Conference

Conference	Joint international meeting on Japanese Classification Society and the Classification and Data Analysis Group of the Italian Statistical Society, JCS-CLADAG 2012
Country/Territory	Italy
City	Capri Island
Period	3/09/12 → 4/09/12

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2014.

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10.1007/978-3-319-06692-9_18

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Iodice D’Enza, Alfonso ; de Velden, Michel Van ; Palumbo, Francesco. / On joint dimension reduction and clustering of categorical data. Analysis and Modeling of Complex Data in Behavioral and Social Sciences. editor / Akinori Okada ; Claus Weihs ; Donatella Vicari ; Giancarlo Ragozini. 2014. pp. 161-169 (Studies in Classification, Data Analysis, and Knowledge Organization, Vol. 49).

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title = "On joint dimension reduction and clustering of categorical data",

abstract = "There exist several methods for clustering high-dimensional data. One popular approach is to use a two-step procedure. In the first step, a dimension reduction technique is used to reduce the dimensionality of the data. In the second step, cluster analysis is applied to the data in the reduced space. This method may be referred to as the tandem approach. An important drawback of this method is that the dimension reduction may distort or hide the cluster structure. As an alternative, various authors have proposed joint dimension reduction and clustering approaches. In this paper we review some of these existing joint dimension reduction and clustering methods for categorical data in a unified framework that facilitates comparison.",

author = "{Iodice D{\textquoteright}Enza}, Alfonso and {de Velden}, {Michel Van} and Francesco Palumbo",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; Joint international meeting on Japanese Classification Society and the Classification and Data Analysis Group of the Italian Statistical Society, JCS-CLADAG 2012 ; Conference date: 03-09-2012 Through 04-09-2012",

year = "2014",

doi = "10.1007/978-3-319-06692-9_18",

language = "English",

isbn = "9783319066912",

series = "Studies in Classification, Data Analysis, and Knowledge Organization",

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Iodice D’Enza, A, de Velden, MV & Palumbo, F 2014, On joint dimension reduction and clustering of categorical data. in A Okada, C Weihs, D Vicari & G Ragozini (eds), Analysis and Modeling of Complex Data in Behavioral and Social Sciences. Studies in Classification, Data Analysis, and Knowledge Organization, vol. 49, pp. 161-169, Joint international meeting on Japanese Classification Society and the Classification and Data Analysis Group of the Italian Statistical Society, JCS-CLADAG 2012, Capri Island, Italy, 3/09/12. https://doi.org/10.1007/978-3-319-06692-9_18

On joint dimension reduction and clustering of categorical data. / Iodice D’Enza, Alfonso; de Velden, Michel Van; Palumbo, Francesco.
Analysis and Modeling of Complex Data in Behavioral and Social Sciences. ed. / Akinori Okada; Claus Weihs; Donatella Vicari; Giancarlo Ragozini. 2014. p. 161-169 (Studies in Classification, Data Analysis, and Knowledge Organization, Vol. 49).

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

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T1 - On joint dimension reduction and clustering of categorical data

AU - Iodice D’Enza, Alfonso

AU - de Velden, Michel Van

AU - Palumbo, Francesco

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - There exist several methods for clustering high-dimensional data. One popular approach is to use a two-step procedure. In the first step, a dimension reduction technique is used to reduce the dimensionality of the data. In the second step, cluster analysis is applied to the data in the reduced space. This method may be referred to as the tandem approach. An important drawback of this method is that the dimension reduction may distort or hide the cluster structure. As an alternative, various authors have proposed joint dimension reduction and clustering approaches. In this paper we review some of these existing joint dimension reduction and clustering methods for categorical data in a unified framework that facilitates comparison.

AB - There exist several methods for clustering high-dimensional data. One popular approach is to use a two-step procedure. In the first step, a dimension reduction technique is used to reduce the dimensionality of the data. In the second step, cluster analysis is applied to the data in the reduced space. This method may be referred to as the tandem approach. An important drawback of this method is that the dimension reduction may distort or hide the cluster structure. As an alternative, various authors have proposed joint dimension reduction and clustering approaches. In this paper we review some of these existing joint dimension reduction and clustering methods for categorical data in a unified framework that facilitates comparison.

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A2 - Okada, Akinori

A2 - Weihs, Claus

A2 - Vicari, Donatella

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Y2 - 3 September 2012 through 4 September 2012

ER -

On joint dimension reduction and clustering of categorical data

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