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Advances in Data Analysis and Classification

Erschienen in:

01.09.2015 | Regular Article

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

verfasst von: Naoto Yamashita, Shin-ichi Mayekawa

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 3/2015

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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 \(c\)-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.

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Titel: A new biplot procedure with joint classification of objects and variables by fuzzy c-means clustering
verfasst von: Naoto Yamashita
Shin-ichi Mayekawa
Publikationsdatum: 01.09.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Advances in Data Analysis and Classification / Ausgabe 3/2015
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI: https://doi.org/10.1007/s11634-014-0184-4

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