SCR-Apriori for Mining 'Sets of Contrasting Rules'

@article{Aleksandrova2020SCRAprioriFM,
  title={SCR-Apriori for Mining 'Sets of Contrasting Rules'},
  author={Marharyta Aleksandrova and Oleg G. Chertov},
  journal={ArXiv},
  year={2020},
  volume={abs/1912.09817}
}
In this paper, we propose an efficient algorithm for mining novel ‘Set of Contrasting Rules’-pattern (SCR-pattern), which consists of several association rules. This pattern is of high interest due to the guaranteed quality of the rules forming it and its ability to discover useful knowledge. However, SCR-pattern has no efficient mining algorithm. We propose SCR-Apriori algorithm, which results in the same set of SCR-patterns as the state-of-the-art approach, but is less computationally… 

References

SHOWING 1-10 OF 19 REFERENCES
Fast algorithms for mining association rules
TLDR
Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
New Algorithms for Fast Discovery of Association Rules
TLDR
New algorithms for fast association mining, which scan the database only once, are presented, addressing the open question whether all the rules can be efficiently extracted in a single database pass.
Scalable Algorithms for Association Mining
TLDR
Efficient algorithms for the discovery of frequent itemsets which forms the compute intensive phase of the association mining task are presented and the effect of using different database layout schemes combined with the proposed decomposition and traverse techniques are presented.
Sets of Contrasting Rules to Identify Trigger Factors
TLDR
A new pattern, referred to as “set of contrasting rules”, is introduced, which allows to easily identify trigger factors: factors that can bring some event state changes and can thus be used to influence the values of some attributes in real applications.
Integrating Classification and Association Rule Mining
TLDR
The integration is done by focusing on mining a special subset of association rules, called class association rules (CARs), and shows that the classifier built this way is more accurate than that produced by the state-of-the-art classification system C4.5.
Sets of Contrasting Rules: A Supervised Descriptive Rule Induction Pattern for Identification of Trigger Factors
TLDR
This work shows that the proposed pattern methodologically belongs to the supervised descriptive rules induction paradigm, and shows that "set of contrasting rules" can be considered as a way to filter the huge amount of association rules and can be used to identify trigger factors.
Efficient mining of emerging patterns: discovering trends and differences
TLDR
It is believed that EPs with low to medium support, such as 1%-20%, can give useful new insights and guidance to experts, in even “well understood” applications.
Algorithms for association rule mining — a general survey and comparison
TLDR
The fundamentals of asso iation rule mining are explained and a general framework is derived and it turns out that the runtime behavior of the algorithms is more similar as to be expe ted.
Frequent Pattern Mining Algorithms: A Survey
TLDR
This chapter will provide a detailed survey of frequent pattern mining algorithms, covering a wide variety of algorithms such as Eclat, TreeProjection, and FP-growth starting from Apriori.
Comparative Survey on Association Rule Mining Algorithms
TLDR
The respective characteristics and the shortcomings of the algorithms for mining association rules are discussed and a comparative study of different association rule mining techniques is provided stating which algorithm is best suitable in which case.
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