The role of Bayesian and frequentist multivariate modeling in statistical data mining

This paper presents an overview of Bayesian and frequentist issues that arise in multivariate statistical modeling involving data mining. We discuss data cubes, structured query language computer commands, and the acquisition of data that violate the usual i.i.d. modeling assumptions. We address problems of multivariate exploratory data analysis, the analysis of non-experimental multivariate data, general statistical problems in data mining, high dimensional issues, graphical models, dimension reduction through conditioning, prediction, Bayesian data mining assuming variable independence, hidden Markov models, and data mining priors. There is also a discussion of some new applications of data mining in the field of Home Security.