Efficient search for association rules

@inproceedings{Webb2000EfficientSF,
  title={Efficient search for association rules},
  author={Geoffrey I. Webb},
  booktitle={KDD '00},
  year={2000}
}
This paper argues that for some applications direct search for association rules can be more e cient than the tw o stage process of the Apriori algorithm which rst nds large itemsets whic hare then used to iden tify associations. In particular, it is argued, Apriori can impose large computational overheads when the number of frequen titemsets is very large. This will often be the case when association rule analysis is performed on domains other than basket analysis or when it is performed for… 
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References

SHOWING 1-10 OF 38 REFERENCES
An Efficient Algorithm for Mining Association Rules in Large Databases
TLDR
This paper presents an efficient algorithm for mining association rules that is fundamentally different from known algorithms and not only reduces the I/O overhead significantly but also has lower CPU overhead for most cases.
An E ective Hash-Based Algorithm for Mining Association RulesJong
In this paper we examine the issue of mining association rules among items in a large database of sales transactions The mining of association rules can be mapped into the problem of discovering
Mining association rules between sets of items in large databases
TLDR
An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
Constraint-based rule mining in large, dense databases
TLDR
A new algorithm is described that directly exploits all user-specified constraints including minimum support, minimum confidence, and a new constraint that ensures every mined rule offers a predictive advantage over any of its simplifications.
Mining the most interesting rules
TLDR
It is argued that by returning a broader set of rules than previous algorithms, these techniques allow for improved insight into the data and support more user-interaction in the optimized rule-mining process.
An effective hash-based algorithm for mining association rules
TLDR
The number of candidate 2-itemsets generated by the proposed algorithm is, in orders of magnitude, smaller than that by previous methods, thus resolving the performance bottleneck, and allows us to effectively trim the transaction database size at a much earlier stage of the iterations, thereby reducing the computational cost for later iterations significantly.
Inclusive pruning: A new class of pruning axiom for unordered search and its application to classificatio n learning.
This paper presents a new class of pruning rule for unordered search. Previous pruning rules for unordered search identify operators that should not be applied in order to prune nodes reached via
Learning Decision Lists Using Homogeneous Rules
TLDR
It is proved that the problem of finding a maximally accurate decision list can be reduced to the problemOf finding maximally inaccurate homogeneous rules, rules whose classification accuracy does not change with their position in the decision list.
Search through Systematic Set Enumeration
TLDR
The Set-Enumerations (SE)tree is presented as a vehicle for representing sets and/or enumerating them in a best-first fashion and its usefulness as the basis for a unifying search-based framework for domains where minimal (maximal) elements of a power set are targeted.
RL4: a tool for knowledge-based induction
  • S. Clearwater, F. Provost
  • Computer Science
    [1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence
  • 1990
TLDR
The induction program RL4 is used as an induction tool, and several examples of its past and present uses are presented, and the directions in which RL4 can go in the future are considered.
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