Agglomerative Hierarchical Clustering: An Introduction to Essentials. (3) Standardization, Normalization and Dimensionality Reduction of a Data Matrix

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Refat Aljumily
Refat Aljumily
1 Newcastle University

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In a previous tutorial article I looked at a proximity coefficient and, in the light of that proximity created a vector-distance matrix and used it to construct a hierarchical tree using different hierarchical clustering methods which will be the basis for exploratory multivariate analysis. The present article deals with three topics: (i) standardization for variable scales variation, (ii) normalization for sample length variation, and (iii) dimensionality reduction or minimization of data space. These techniques reflect the author’s academic background and particular area of interest and are, by necessity, not a particular purpose and are straightforwardly applicable to other kinds of data, and thus to a wide range of analysis in Linguistics. My treatment of these techniques is, necessarily, introductory and brief. I hope that this article will provide practitioners with an introductory overview of these techniques used for cluster analysis of electronic corpora of linguistic data.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

Refat Aljumily. 2016. \u201cAgglomerative Hierarchical Clustering: An Introduction to Essentials. (3) Standardization, Normalization and Dimensionality Reduction of a Data Matrix\u201d. Global Journal of Human-Social Science - G: Linguistics & Education GJHSS-G Volume 16 (GJHSS Volume 16 Issue G3): .

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GJHSS Volume 16 Issue G3
Pg. 55- 63
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Crossref Journal DOI 10.17406/GJHSS

Print ISSN 0975-587X

e-ISSN 2249-460X

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GJHSS-G Classification: FOR Code: 139999
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v1.2

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April 29, 2016

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English

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In a previous tutorial article I looked at a proximity coefficient and, in the light of that proximity created a vector-distance matrix and used it to construct a hierarchical tree using different hierarchical clustering methods which will be the basis for exploratory multivariate analysis. The present article deals with three topics: (i) standardization for variable scales variation, (ii) normalization for sample length variation, and (iii) dimensionality reduction or minimization of data space. These techniques reflect the author’s academic background and particular area of interest and are, by necessity, not a particular purpose and are straightforwardly applicable to other kinds of data, and thus to a wide range of analysis in Linguistics. My treatment of these techniques is, necessarily, introductory and brief. I hope that this article will provide practitioners with an introductory overview of these techniques used for cluster analysis of electronic corpora of linguistic data.

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Agglomerative Hierarchical Clustering: An Introduction to Essentials. (3) Standardization, Normalization and Dimensionality Reduction of a Data Matrix

Refat Aljumily
Refat Aljumily

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