Cluster Correspondence Analysis

Velden, Michel van de; D’Enza, Alfonso Iodice; Palumbo, Francesco

doi:10.1007/s11336-016-9514-0

Cited by 64 publications

(41 citation statements)

References 19 publications

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“…Below we give a brief description of these methods. For more details on the methods, their relationships as well as an appraisal of their performance in simulated experiments, see Van de Velden et al (2017). For the analysis of categorical data, some additional notation is necessary.…”

Section: Methods For Categorical Datamentioning

confidence: 99%

“…Note that we do not concern ourselves here with choosing a "best" method. An appraisal of the methods for continuous data can be found in Timmerman, Ceulemans, Kiers, and Vichi (2010), whereas Van de Velden et al (2017) consider the performance of the methods for categorical data.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, a joint display of the row and column points may not be very informative. Following proposals by Gower, Groenen, and Van de Velden (2010) and Gower et al (2011), Van de Velden et al (2017 propose to multiply the cluster mean points by a constant γ and the categories by its inverse, in such a way that the average squared deviation from the origin is the same in both sets of points. That is,…”

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confidence: 99%

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Beyond Tandem Analysis: Joint Dimension Reduction and Clustering in R

Markos¹,

D’Enza²,

Velden³

2019

J. Stat. Soft.

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We present the R package clustrd which implements a class of methods that combine dimension reduction and clustering of continuous or categorical data. In particular, for continuous data, the package contains implementations of factorial K-means and reduced K-means; both methods combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means, i-FCB and cluster correspondence analysis, which combine multiple correspondence analysis with K-means. Two examples on real data sets are provided to illustrate the usage of the main functions.

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Section: Methods For Categorical Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Beyond Tandem Analysis: Joint Dimension Reduction and Clustering in R

Markos¹,

D’Enza²,

Velden³

2019

J. Stat. Soft.

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“…Such visualizations may be useful with respect to cluster interpretation. Examples can be found in Vichi and Kiers (2001) and Timmerman, Ceulemans, Kiers, and Vichi (2010), for numerical data, Hwang et al (2006), van Dam and van de Velden (2015) and van de Velden et al (2017) for categorical data, and Vichi et al (2009), for mixed data.…”

Section: Discussionmentioning

confidence: 99%

Distance‐based clustering of mixed data

Velden

D’Enza

Markos

2018

WIREs Computational Stats

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Cluster analysis comprises of several unsupervised techniques aiming to identify a subgroup (cluster) structure underlying the observations of a data set. The desired cluster allocation is such that it assigns similar observations to the same subgroup. Depending on the field of application and on domain‐specific requirements, different approaches exist that tackle the clustering problem. In distance‐based clustering, a distance metric is used to determine the similarity between data objects. The distance metric can be used to cluster observations by considering the distances between objects directly or by considering distances between objects and cluster centroids (or some other cluster representative points). Most distance metrics, and hence the distance‐based clustering methods, work either with continuous‐only or categorical‐only data. In applications, however, observations are often described by a combination of both continuous and categorical variables. Such data sets can be referred to as mixed or mixed‐type data. In this review, we consider different methods for distance‐based cluster analysis of mixed data. In particular, we distinguish three different streams that range from basic data preprocessing (where all variables are converted to the same scale), to the use of specific distance measures for mixed data, and finally to so‐called joint data reduction (a combination of dimension reduction and clustering) methods specifically designed for mixed data. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical and Graphical Methods of Data Analysis > Dimension Reduction

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“…Recently, there appears to be an increased interest in joint methods. See, for example, (Hwang et al 2006;Hwang 2014, 2017;Vichi and Kiers 2001;van de Velden et al 2017). In this special issue, we present several contributions covering various aspects of joint dimension reduction and cluster analysis methods.…”

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confidence: 99%