6533b855fe1ef96bd12b094b

RESEARCH PRODUCT

Beyond Tandem Analysis: Joint Dimension Reduction and Clustering in R

Michel Van De VeldenAlfonso Iodice D'enzaAngelos Markos

subject

dimension reduction; clustering; principal component analysis; multiple correspondence analysis; K-meansStatistics and Probabilitydimension reduction clustering principal component analysis multiple correspon-dence analysis K-meansFactorialmultiple correspon-dence analysisMultiple correspondence analysiComputer sciencedimension reductionprincipal component analysisk-meansmultiple correspondence analysisPrincipal component analysicomputer.software_genre01 natural sciencesCorrespondence analysis010104 statistics & probabilityMultiple correspondence analysis0101 mathematicsCluster analysisCategorical variablelcsh:Statisticslcsh:HA1-4737Dimensionality reductionk-means clusteringK-meanPrincipal component analysisData miningHA29-32Statistics Probability and UncertaintycomputerSoftwareclustering

description

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.

yearjournalcountryeditionlanguage
2019-01-01Journal of Statistical Software
10.18637/jss.v091.i10https://www.jstatsoft.org/index.php/jss/article/view/2915