%0 Book Section
%T From Conventional Data Analysis Methods to Big Data Analytics
%+ CEDRIC. Méthodes statistiques de data-mining et apprentissage (CEDRIC - MSDMA)
%A Saporta, Gilbert
%@ 9781786300737
%B Big Data for Insurance Companies
%E Marine Corlosquet‐Habart
%E Jacques Janssen
%I John Wiley & Sons, Inc.
%C Hoboken
%P 27-41
%8 2018-01-19
%D 2018
%R 10.1002/9781119489368.ch2
%K Big Data Processing
%K cross validation
%K data mining
%K supervised classificatio
%Z Mathematics [math]/Statistics [math.ST]Book sections
%X 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.
%G English
%2 https://cnam.hal.science/hal-02470097/document
%2 https://cnam.hal.science/hal-02470097/file/04_Chapter%202_ENG_revGSavril2020.pdf
%L hal-02470097
%U https://cnam.hal.science/hal-02470097
%~ CNAM
%~ INSMI
%~ CEDRIC-CNAM
%~ HESAM-CNAM
%~ HESAM