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Incrementally updating the discovered sequential patterns based on pre-large concept.

, , , , and . Intell. Data Anal., 19 (5): 1071-1089 (2015)

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An efficient algorithm to maintain the discovered frequent sequences with record deletion., , , , and . Intell. Data Anal., 20 (3): 655-677 (2016)Fast algorithms for mining high-utility itemsets with various discount strategies., , , , and . Advanced Engineering Informatics, 30 (2): 109-126 (2016)Inferring social network user profiles using a partial social graph., , , and . J. Intell. Inf. Syst., 47 (2): 313-344 (2016)Frequent Itemset Mining in Big Data With Effective Single Scan Algorithms., , , and . IEEE Access, (2018)A Survey of Parallel Sequential Pattern Mining., , , , and . TKDD, 13 (3): 25:1-25:34 (2019)Efficiently Updating the Discovered Sequential Patterns for Sequence Modification., , , and . International Journal of Software Engineering and Knowledge Engineering, 26 (8): 1285-1314 (2016)Using Multi-Conditional Minimum Thresholds in Temporal Fuzzy Utility Mining., , , and . Int. J. Comput. Intell. Syst., 12 (2): 613-626 (2019)Mining Minimal High-Utility Itemsets., , , , and . DEXA (1), volume 9827 of Lecture Notes in Computer Science, page 88-101. Springer, (2016)EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining., , , , and . MICAI (1), volume 9413 of Lecture Notes in Computer Science, page 530-546. Springer, (2015)More Accurate Inference of User Profiles in Online Social Networks., , , and . MICAI (2), volume 9414 of Lecture Notes in Computer Science, page 533-546. Springer, (2015)