Methods of Data Analysis

Elaine Fehrman (), Vincent Egan (), Alexander N. Gorban (), Jeremy Levesley (), Evgeny M. Mirkes () and Awaz K. Muhammad ()
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Elaine Fehrman: Rampton Hospital, Men’s Personality Disorder and National Women’s Directorate
Vincent Egan: University of Nottingham, Department of Psychiatry and Applied Psychology
Alexander N. Gorban: University of Leicester, Department of Mathematics
Jeremy Levesley: University of Leicester, Department of Mathematics
Evgeny M. Mirkes: University of Leicester, Department of Mathematics
Awaz K. Muhammad: University of Leicester, Department of Mathematics

Chapter Chapter 3 in Personality Traits and Drug Consumption, 2019, pp 35-59 from Springer

Abstract: Abstract In this chapter, we give a brief outline of the methods of data analysis used, from elementary T-scores to nonlinear principal component analysis (PCA), including data normalisation, quantification of categorical attributes, categorical principal component analysis (CatPCA), sparse PCA, the method of principal variables, the original ‘double’ Kaiser selection rule, k-nearest neighbours for various distances, decision tree with various split criteria (information gain, Gini gain or DKM gain), linear discriminant analysis, Gaussian mixture, probability density function estimation by radial basis functions, logistic regression, naïve Bayes approach, random forest, and data visualisation on the nonlinear PCA canvas.

Keywords: Preprocessing; Normalisation; Transformation; Quantification; Principal components; Classification; Visualisation (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/978-3-030-10442-9_3

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