On the complexity of inducing categorical and quantitative association rules

Inducing association rules is one of the central tasks in data mining applications. Quantitative association rules induced from databases describe rich and hidden relationships to be found within data that can prove useful for various application purposes (e.g., market basket analysis, customer profiling, and others). Although association rules are quite widely used in practice, a thorough analysis of the related computational complexity is missing. This paper intends to provide a contribution in this setting. To this end, we first formally define quantitative association rule mining problems, which include boolean association rules as a special case; we then analyze computational complexity of such problems. The general problem as well as some interesting special cases are considered.