Homogeneous Discoveries Contain No Surprises: Inferring Risk Profiles from Large Databases

Arno Siebes

Abstract:

Many models of reality are probabilistic. For example, not everyone orders crisps with their beer, but a certain percentage does. Inferring such probabilistic knowledge from databases is one of the major challenges for data mining.

Recently Agrawal et al. [1] investigated a class of such problems. In this paper a new class of such problems is investigated, viz., inferring risk-profiles. The prototypical example of this class is: "what is the probability that a given policy-holder will file a claim with the insurance company in the next year". A risk-profile is then a description of a group of insurants that have the same probability for filing a claim.

It is shown in this paper that homogeneous descriptions are the most plausible risk-profiles. Moreover, under modest assumptions it is shown that covers of such homogeneous descriptions are essentially unique. A direct consequence of this result is that it suffices to search for the homogeneous description with the highest associated probability.

The main result of this paper is thus that we show that the inference problem for risk-profiles reduces to the well studied problem of maximising a quality function.