**Discovering Top-k Rules using Subjective and Objective Criteria

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Abstract

This paper studies two questions about rule discovery. Can we characterize the usefulness of rules using quantitative criteria? How can we discover rules using those criteria? As a testbed, we consider entity enhancing rules (REEs), which subsume common association rules and data quality rules as special cases. We characterize REEs using a bi-criteria model, with both objective measures such as support and confidence, and subjective measures for the user’s needs; we learn the subjective measure and the weight vectors via active learning. Based on the bi-criteria model, we develop a top-k algorithm to discover top-ranked REEs, and an any-time algorithm for successive discovery via lazy evaluation. We parallelize these algorithms such that they guarantee to reduce runtime when more processors are used. Using real-life and synthetic datasets, we show that the algorithms are able to find top-ranked rules and speed up conventional rule-discovery methods by 134X on average.

Wenfei Fan, Ziyan Han, Yaoshu Wang, and Min Xie, 2023. Discovering Top-k Rules using Subjective and Objective Criteria. Proceedings of the ACM on Management of Data (SIGMOD), 1(1), pp.1-29.