Efficient mining of extraordinary patterns by pruning and predicting

Pattern mining is an important data mining technology. The existing pattern mining algorithms mainly focus on discovery of ordinary patterns in databases, for example, frequent pattern mining finds patterns with high frequencies and utility pattern mining discovers patterns with high utilities. However, in many real-world applications, people are more interested in finding extraordinary patterns with low frequencies and high utilities or with high frequencies and low utilities. While mining ordinary patterns is computationally hard, it is even harder to mine extraordinary patterns. In particular, a two-phase approach that first generates and materializes candidates (high-frequency patterns or high-utility patterns) and then finds extraordinary patterns from the candidates, suffers from the scalability and efficiency bottlenecks. This paper proposes an efficient algorithm for mining extraordinary patterns. The novelty of our algorithm lies in newly proposed lower bounds both on frequencies and on utilities of patterns, new pruning strategies and new predicting strategies for dramatically reducing the search space, and a novel data structure for efficient computation. The proposed algorithm employs a single-phase approach without materializing candidates, and also adapts the upper bounds on supports and on utilities for pruning. Extensive experiments show that the new pruning and predicting strategies are effective, and the proposed algorithm is efficient and scalable. (C) 2019 Elsevier Ltd. All rights reserved.