Discovery of frequent DATALOG patterns

Discovery of frequent patterns has been studied in a variety of data mining settings. In its simplest form, known from association rule mining, the ta sk is to discover all frequent itemsets, i.e., all combinations of items th at are found in a sufficient number of examples. The fundamental task of as sociation rule and frequent set discovery has been extended in various dire ctions, allowing more useful patterns to be discovered with special purpose algorithms. We present WARMR, a general purpose inductive logic programmin g algorithm that addresses frequent query discovery: a very general DATALOG formulation of the frequent pattern discovery problem. The motivation for this novel approach is twofold. First, exploratory data mining isi well supported: WARMR offers the flexibility required to experim ent with standard and in particular novel settings not supported by special purpose algorithms. Also, application prototypes based on WARMR can be use d as benchmarks in the comparison and evaluation of new special purpose alg orithms. Second, the unified representation gives insight to the blurred pi cture of the frequent pattern discovery domain. Within the DATALOG formulat ion a number of dimensions appear that relink diverged settings. We demonstrate the frequent query approach and its use on two applications, one in alarm analysis, and one in a chemical toxicology domain.