%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