Efficient algorithms for mining frequent high utility sequences with constraints

• Published in: Information sciences Vol. 568; pp. 239 - 264
• Main Authors: Truong, Tin, Duong, Hai, Le, Bac, Fournier-Viger, Philippe, Yun, Unil, Fujita, Hamido
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01-08-2021
• Subjects: Frequent closed high utility sequence; Frequent generator high utility sequence; Frequent high utility sequence; Mining patterns with constraints; Quantitative sequential database; Frequent high utility sequence; Frequent generator high utility sequence; Mining patterns with constraints; Frequent closed high utility sequence; Quantitative sequential database
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2021.01.060

•A search space partitioning approach for frequent high utility sequence mining with constraints.•The C-FHUSM algorithm to mine these patterns from generators and closed sequences.•The FGenHUSM algorithm to mine all frequent generator high utility sequences.
An important data mining task is to discover all high utility sequences in a quantitative sequence database. Although useful, the number of discovered sequences is often very large. To find patterns that are more tailored to a user’s needs, this paper studies the problem of mining frequent high utility sequences satisfying item constraints. This article proposes a novel algorithm named C-FHUSM to quickly obtain these sequences from two concise representations discovered from a quantitative sequence database, namely frequent generator high utility sequences and frequent closed high utility sequences. The first set is extracted using a novel algorithm named FGenHUSM, while an existing algorithm is applied to extract the second set. C-FHUSM integrates novel pruning techniques to ignore sequences that do not satisfy item constraints early by checking only a small number of representative sequences at the beginning of the mining process. Experimental results show that C-FHUSM can be more than ten times faster and has better scalability than a modified version of the state-of-the-art EHUSM algorithm for mining sequences with item constraints. Moreover, it is found that using C-FHUSM is beneficial when a user frequently changes constraints as results can be updated without rescanning the database.