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

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...

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Bibliographic Details
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
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Summary: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 .
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2023.3241213