On the Interface between Cluster Analysis, Principal Component Analysis, and Multidimensional Scaling

Bock, Hans‐Hermann

doi:10.1007/978-94-009-3977-6_2

Cited by 57 publications

(31 citation statements)

References 8 publications

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“…17, Diday andSchroeder (1974a)) and clusterwise regression (Bock (1969), Charles (1977), Späth (1979)). -projection pursuit clustering where class centers are located on a lowdimensional hyperplane (Bock (1987, 1996c), Vichi (2005), -characterizing a class by the most typical subset (pair, triple,...) of objects from this class (Diday et al (1979)). …”

Section: Generalized K-means Methodsmentioning

confidence: 99%

Clustering Methods: A History of k-Means Algorithms

Bock

2007

Studies in Classification, Data Analysis, and Knowledge Organization

161

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Section: Generalized K-means Methodsmentioning

confidence: 99%

Clustering Methods: A History of k-Means Algorithms

Bock

2007

Studies in Classification, Data Analysis, and Knowledge Organization

161

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“…A second illustration pertains to RKM (De Soete & Carroll, 1994;Timmerman, Ceulemans, Kiers, & Vichi, 2010), also called projection pursuit clustering (Bock, 1987). In this model, the variables are reduced to a limited set of components, and simultaneously the objects are clustered on the basis of the scores of the objects on these components.…”

Section: Reduced K-meansmentioning

confidence: 99%

CHull: A generic convex-hull-based model selection method

2012

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When analyzing data, researchers are often confronted with a model selection problem (e.g., determining the number of components/factors in principal components analysis [PCA]/factor analysis or identifying the most important predictors in a regression analysis). To tackle such a problem, researchers may apply some objective procedure, like parallel analysis in PCA/factor analysis or stepwise selection methods in regression analysis. A drawback of these procedures is that they can only be applied to the model selection problem at hand. An interesting alternative is the CHull model selection procedure, which was originally developed for multiway analysis (e.g., multimode partitioning). However, the key idea behind the CHull procedure-identifying a model that optimally balances model goodness of fit/misfit and model complexity-is quite generic. Therefore, the procedure may also be used when applying many other analysis techniques. The aim of this article is twofold. First, we demonstrate the wide applicability of the CHull method by showing how it can be used to solve various model selection problems in the context of PCA, reduced Kmeans, best-subset regression, and partial least squares regression. Moreover, a comparison of CHull with standard model selection methods for these problems is performed. Second, we present the CHULL software, which may be downloaded from http://ppw.kuleuven.be/okp/software/CHULL/, to assist the user in applying the CHull procedure.Keywords Model selection . CHULL . Graphical user interface . PCA . Regression . PLS When analyzing data, researchers very often face a (complex) model selection problem. Take, as a first example, a clinical psychologist who wants to study the dimensionality of a particular psychological construct, such as alexithymia (i.e., having difficulties distinguishing between and expressing emotions). To this end, the researcher administers to a sample of subjects a questionnaire that measures the construct in question. Next, the researcher may examine the internal structure of the questionnaire and the dimensionality of the underlying construct by performing a principal components analysis (PCA) or an exploratory factor analysis (EFA) on the collected data. In doing so, the researcher needs to determine the optimal number of components or factors, and thus has to solve a model selection problem. As a second example, consider an economist who wants to assess which "factors" affect the selling price of a house in a particular neighborhood. To study this, the researcher may collect data (e.g., price and size of the house or the number of rooms) about a sample of houses from the neighborhood under study and may perform a regression analysis. In this case, again a model selection problem arises, which consists of selecting the (sub)set of predictors (optionally also including interactions between the predictors) that optimally predicts the selling price of a house.In general, model selection boils down to selecting, out of a set of models, one that yields a good...

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“…When we have low dimensional spaces these methods aren't able to work well. There are two main approaches for subspaces methods: in first class centers are considered on a same unknown subspace and in second each class is located on specific subspace [46]. The idea of subtopics or subgroups is appropriate for document clustering and text mining [47].…”

Section: Recent Workmentioning

confidence: 99%