RP-growth: Top-k Mining of Relevant Patterns with Minimum Support Raising

One practical inconvenience in frequent pattern mining is that it often yields a flood of common or uninformative patterns, and thus we should carefully adjust the minimum support. To alleviate this inconvenience, based on FP-growth, this paper proposes RP-growth, an efficient algorithm for top-k mining of discriminative patterns which are highly relevant to the class of interest. RP-growth conducts a branchand-bound search using anti-monotonic upper bounds of the relevance scores such as F-score and χ, and the pruning in branch-and-bound search is successfully translated to minimum support raising, a standard, easy-to-implement pruning strategy for top-k mining. Furthermore, by introducing the notion called weakness and an additional, aggressive pruning strategy based on weakness, RP-growth efficiently finds k patterns of wide variety and high relevance to the class of interest. Experimental results on text classification exhibit the efficiency and the usefulness of RP-growth.