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From Conventional Data Analysis Methods to Big Data Analytics


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)



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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