On Fuzzy Clustering based Correlation

Sato‐Ilic, Mika

doi:10.1016/j.procs.2012.09.061

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…The cluster-scaled PCA utilizes this advantage of fuzzy clustering. A numerical example shows a better performance for the cluster-scaled PCA, and other examples are shown in many pieces of literature (Sato-Ilic, 2010, 2011a, 2011b, 2012a, 2012b.…”

Section: Numerical Examplementioning

confidence: 81%

“…In particular, for the high‐dimension and low‐sample size data, conventional PCA theoretically cannot obtain correct solutions (Ahn et al, 2007; Baik et al, 2005; Hall et al, 2005; Tong et al, 2014; Welsh et al, 2001). In order to solve this problem, we have proposed several cluster‐scaled PCA for high‐dimension and low‐sample size data (Sato‐Ilic, 2011a, 2011b, 2012a, 2012b) and obtained a better performance using an idea of symbolic data (Billard & Diday, 2007; Bock & Diday, 2000; Diday, 2016).…”

Section: Cluster‐scaled Pcamentioning

confidence: 99%

“…In order to solve these problems, in this article, we introduce the simple and reduced complexity of driving the solutions (this means that we do not need a large number of parameters) by using an idea of a cluster-scaled metric. This metric is essentially utilizing the obtained cluster after the clustering of data as a metric to use another method, which is the PCA in this article, and obtain a more accurate result or result which we cannot obtain by using an ordinary method (Sato-Ilic, 2010, 2011a, 2011b, 2012a, 2012b. This article's structure is as follows: in Section 2, we describe fuzzy clustering, which we will use in this article as a clustering method.…”

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confidence: 99%

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Cluster‐scaled principal component analysis

Sato‐Ilic

2021

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Cluster‐scaled analysis means exploiting the cluster‐based scaling to conventional data analysis to obtain more accurate results or results that we cannot obtain by using ordinary analysis. Our target data is complex and large amounts of data. For this type of data, it is well known that ordinary statistical methods do not always work well, or theoretically, we know that we cannot obtain a correct result. As a tool of this implementation, we utilize fuzzy clustering, which is well known as a robust clustering to a complex and large amount of data. That is, we use the fuzzy clustering result as a scale of data and apply the rescaled data by the cluster‐scale to another target analysis. Our target analysis in this article is principal component analysis, which is a well‐known dimensional reduction method. A numerical example shows a better performance of the cluster‐scaled principal component analysis. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification

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