MCoR-Miner: Maximal Co-Occurrence Nonoverlapping Sequential Rule Mining

• Published in: IEEE transactions on knowledge and data engineering 2023-09, Vol.35 (9), p.1-16
• Main Authors: Li, Yan, Zhang, Chang, Li, Jie, Song, Wei, Qi, Zhenlian, Wu, Youxi, Wu, Xindong
• Format: Article
• Language: English
• Subjects: Algorithms; Business competition; co-occurrence pattern; Data mining; Enumeration; Itemsets; Mathematical analysis; maximal rule mining; Mining; Pattern analysis; Pattern matching; Prediction algorithms; Protein sequence; rule-antecedent; Searching; Sequences; Sequential pattern mining; sequential rule mining; Spatial databases; Strategy; Time series analysis
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2023.3241213

The aim of sequential pattern mining (SPM) is to discover potentially useful information from a given sequence. Although various SPM methods have been investigated, most of these focus on mining all of the patterns. However, users sometimes want to mine patterns with the same specific prefix pattern, called co-occurrence pattern. Since sequential rule mining can make better use of the results of SPM, and obtain better recommendation performance, this paper addresses the issue of maximal co-occurrence nonoverlapping sequential rule (MCoR) mining and proposes the MCoR-Miner algorithm. To improve the efficiency of support calculation, MCoR-Miner employs depth-first search and backtracking strategies equipped with an indexing mechanism to avoid the use of sequential searching. To obviate useless support calculations for some sequences, MCoR-Miner adopts a filtering strategy to prune the sequences without the prefix pattern. To reduce the number of candidate patterns, MCoR-Miner applies the frequent item and binomial enumeration tree strategies. To avoid searching for the maximal rules through brute force, MCoR-Miner uses a screening strategy. To validate the performance of MCoR-Miner, eleven competitive algorithms were conducted on eight sequences. Our experimental results showed that MCoR-Miner outperformed other competitive algorithms, and yielded better recommendation performance than frequent co-occurrence pattern mining. All algorithms and datasets can be downloaded from https://github.com/wuc567/Pattern-Mining/tree/master/MCoR-Miner .