https://cnam.hal.science/hal-02470097Saporta, GilbertGilbertSaportaCEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage - CEDRIC - Centre d'études et de recherche en informatique et communications - ENSIIE - Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise - CNAM - Conservatoire National des Arts et Métiers [CNAM] - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers universitéFrom Conventional Data Analysis Methods to Big Data AnalyticsHAL CCSD2018Big Data Processingcross validationdata miningsupervised classificatio[MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]Rigaux, PhilippeMarine Corlosquet‐HabartJacques Janssen2020-04-09 17:37:522022-09-28 05:53:232020-04-15 11:24:27enBook sectionshttps://cnam.hal.science/hal-02470097/document10.1002/9781119489368.ch2application/pdf1Data 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.