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Mining sequential rules with itemset constraints.
- Source :
- Applied Intelligence; Oct2021, Vol. 51 Issue 10, p7208-7220, 13p
- Publication Year :
- 2021
-
Abstract
- Mining sequential rules from a sequence database usually returns a set of rules with great cardinality. However, in real world applications, the end-users are often interested in a subset of sequential rules. Particularly, they may consider only rules that contain a specific set of items. The naïve strategy is to apply such itemset constraints into the post-processing step. However, such approaches require much effort and time. This paper proposes the effective methods for integrating itemset constraints into the actual mining process. We proposed two algorithms, namely MSRIC-R and MSRIC-P, to solve this problem in which MSRIC-R pushed the constraints into the rule generating phase, and MSRIC-P pushes into the pattern mining phase. Experiments show that the proposed algorithms outperform the post-processing approach. [ABSTRACT FROM AUTHOR]
- Subjects :
- SEQUENTIAL pattern mining
PROBLEM solving
PROCESS mining
ALGORITHMS
DATABASES
Subjects
Details
- Language :
- English
- ISSN :
- 0924669X
- Volume :
- 51
- Issue :
- 10
- Database :
- Complementary Index
- Journal :
- Applied Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 152275087
- Full Text :
- https://doi.org/10.1007/s10489-020-02153-w