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Discovery of frequent DATALOG patterns

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Abstract

Discovery of frequent patterns has been studied in a variety of data mining settings. In its simplest form, known from association rule mining, the task is to discover all frequent itemsets, i.e., all combinations of items that are found in a sufficient number of examples. The fundamental task of association rule and frequent set discovery has been extended in various directions, allowing more useful patterns to be discovered with special purpose algorithms. We present WARMR, a general purpose inductive logic programming 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 is well supported: WARMR offers the flexibility required to experiment with standard and in particular novel settings not supported by special purpose algorithms. Also, application prototypes based on WARMR can be used as benchmarks in the comparison and evaluation of new special purpose algorithms. Second, the unified representation gives insight to the blurred picture of the frequent pattern discovery domain. Within the DATALOG formulation 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.

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  1. Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, B-3001, Heverlee, Belgium

    Luc Dehaspe

  2. Rolf Nevanlinna Institute & Department of Computer Science, University of Helsinki, P.O. Box 4, FIN-00014, Finland

    Hannu Toivonen

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Dehaspe, L., Toivonen, H. Discovery of frequent DATALOG patterns. Data Mining and Knowledge Discovery 3, 7–36 (1999). https://doi.org/10.1023/A:1009863704807

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