A new biplot procedure with joint classification of objects and variables by fuzzy c-means clustering

Naoto Yamashita () and Shin-ichi Mayekawa

Advances in Data Analysis and Classification, 2015, vol. 9, issue 3, 243-266

Abstract: Biplot is a technique for obtaining a low-dimensional configuration of the data matrix in which both the objects and the variables of the data matrix are jointly represented as points and vectors, respectively. However, biplots with a large number of objects and variables remain difficult to interpret. Therefore, in this research, we propose a new biplot procedure that allows us to interpret a large data matrix. In particular, the objects and variables are classified into a small number of clusters by using fuzzy $$c$$ c -means clustering and the resulting clusters are simultaneously biplotted in lower-dimensional space. This procedure allows us to understand the configurations easily and to grasp the fuzzy memberships of the objects and variables to the clusters. A simulation study and real data example are also provided to demonstrate the effectiveness of the proposed procedure. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Biplot; Fuzzy $$c$$ c -means clustering; Principal component analysis; Singular value decomposition; Alternating least squares; 62A86; 65F15; 62H25; 15A18 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11634-014-0184-4

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