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From Conventional Data Analysis Methods to Big Data Analytics

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Abstract

Data analysis in this chapter mainly means descriptive and exploratory methods, also known as unsupervised. The objective is to describe as well as structure a set of data that can be represented in the form of a rectangular table crossing n statistical units and p variables. Data analysis methods are essentially dimension reduction methods that are divided into two categories: factor methods; and the unsupervised classification methods or clustering. Data mining is a step in the knowledge discovery process, which involves applying data analysis algorithms. Data mining seeks to find predictive models of a Y denoted response, but from a very different perspective than that of conventional modeling. This chapter distinguishes regression methods where Y is quantitative, supervised classification methods (also called discrimination methods) where Y is categorical, most often with two modalities. The chapter also discusses new tools for big data processing, based on validation with data set aside.
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hal-02470097 , version 1 (09-04-2020)

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Gilbert Saporta. From Conventional Data Analysis Methods to Big Data Analytics. Marine Corlosquet‐Habart; Jacques Janssen. Big Data for Insurance Companies, John Wiley & Sons, Inc., pp.27-41, 2018, 9781786300737. ⟨10.1002/9781119489368.ch2⟩. ⟨hal-02470097⟩
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