hal-02470097
https://cnam.hal.science/hal-02470097
https://cnam.hal.science/hal-02470097/document
https://cnam.hal.science/hal-02470097/file/04_Chapter%202_ENG_revGSavril2020.pdf
doi:10.1002/9781119489368.ch2
[CNAM] Conservatoire National des Arts et Métiers
[INSMI] CNRS-INSMI - INstitut des Sciences Mathématiques et de leurs Interactions
[CEDRIC-CNAM] Centre d'études et de recherche en informatique et communications
[HESAM-CNAM] Conservatoire National des Arts et Métiers - Hesam
[HESAM] Hesam
From Conventional Data Analysis Methods to Big Data Analytics
Saporta, Gilbert
[MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]
COUV
Big Data Processing
cross validation
data mining
supervised classificatio
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.
2018-01-19
2020-04-09
en
Big Data for Insurance Companies
John Wiley & Sons, Inc.