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Clustering (partitioning) and simultaneous dimension reduction of objects and variables of a two-way two-mode data matrix is proposed here. The methodology is based on a general model that includes K-means clustering, factorial K-means, projection pursuit clustering (also known as reduced K-means), principal component analysis and intermediate cases of object clustering and variable reduction. Since...
Reduced K-means (RKM) and Factorial K-means (FKM) are two data reduction techniques incorporating principal component analysis and K-means into a unified methodology to obtain a reduced set of components for variables and an optimal partition for objects. RKM finds clusters in a reduced space by maximizing the between-clusters deviance without imposing any condition on the within-clusters deviance,...
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