Active Sampling for Data Mining

Data mining is a complex process that aims to derive an accurate predictive model starting from a collection of data. Traditional approaches assume that data are given in advance and their quality, size and structure are independent parameters. In this paper we argue that an extended vision of data mining should include the step of data acquisition as part of the overall process. Moreover the static view should be replaced by an evolving perspective that conceives the data mining as an iterative process where data acquisition and data analysis repeatedly follow each other. A decision support tool based on data mining will have to be extended accordingly. Decision making will be concerned not only with a predictive purpose but also with a policy for a next data acquisition step. A successful data acquisition strategy will have to take into account both future model accuracy and the cost associated to the acquisition of each feature. To find a trade off between these two components is an open issue. A framework to focus this new challenging problem is proposed.