Corpus ID: 237532557

Frequent Itemset Mining with Multiple Minimum Supports: a Constraint-based Approach

@article{Belaid2021FrequentIM,
  title={Frequent Itemset Mining with Multiple Minimum Supports: a Constraint-based Approach},
  author={Mohamed-Bachir Belaid and Nadjib Lazaar},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.07844}
}
The problem of discovering frequent itemsets including rare ones has received a great deal of attention. The mining process needs to be flexible enough to extract frequent and rare regularities at once. On the other hand, it has recently been shown that constraint programming is a flexible way to tackle data mining tasks. In this paper, we propose a constraint programming approach for mining itemsets with multiple minimum supports. Our approach provides the user with the possibility to express… Expand

Figures and Tables from this paper

References

SHOWING 1-10 OF 15 REFERENCES
Constraint Programming for Mining Borders of Frequent Itemsets
TLDR
This work proposes a generic framework based on constraint programming to mine both borders of frequent itemsets and proves that the problem of mining borders with additional constraints is coNP-hard, ruling out the hope for the existence of a single CSP solving this problem. Expand
Mining of frequent patterns with multiple minimum supports
TLDR
Substantial experiments show that the proposed algorithms can not only avoid the rare item problem, but also efficiently and effectively discover the complete set of FPs in transactional databases while considering multiple minimum supports and outperform the state-of-the-art CFP-growth++ algorithm in terms of execution time, memory usage and scalability. Expand
Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms
TLDR
An efficient CFP-growth algorithm is proposed by proposing new pruning techniques to reduce the search space and experimental results show that the proposed pruned patterns are effective. Expand
A Global Constraint for Closed Frequent Pattern Mining
TLDR
This paper proposes the ClosedPattern global constraint to capture the closed frequent pattern mining problem without requiring reified constraints or extra variables, and presents an algorithm to enforce domain consistency on ClosedPattern in polynomial time. Expand
CoverSize: A Global Constraint for Frequency-Based Itemset Mining
TLDR
This work introduces the CoverSize constraint for itemset mining problems, a global constraint for counting and constraining the number of transactions covered by the itemset decision variables, and exposes the size of the cover as a variable, which opens up new modelling perspectives compared to an existing global constraint. Expand
Mining Generalized Association Rules with Multiple Minimum Supports
TLDR
The problems of using classic Apriori itemset generation are discussed, two algorithms, MMS_Cumulate and MMS-Stratify, for discovering the generalized frequent itemsets are presented and these two algorithms are shown to be very effective and have good linear scale-up characteristic. Expand
Constraint Programming for Association Rules
TLDR
This paper proposes a declarative model based on constraint programming to capture association rules, and shows that it can capture the popular minimal non-redundant property of association rules. Expand
Constraint programming for itemset mining
The relationship between constraint-based mining and constraint programming is explored by showing how the typical constraints used in pattern mining can be formulated for use in constraintExpand
An improved multiple minimum support based approach to mine rare association rules
TLDR
Experimental results on both synthetic and real world datasets show that the proposed approach improves performance over existing approaches by minimizing the explosion of number of frequent itemsets involving frequent items and without missing the frequent itemset involving rare items. Expand
Mining association rules with multiple minimum supports
TLDR
This paper proposes a novel technique that allows the user to specify multiple minimum supports to reflect the natures of the items and their varied frequencies in the database and shows that the technique is very effective. Expand
...
1
2
...