Algorithms for association rule mining — a general survey and comparison

@article{Hipp2000AlgorithmsFA,
  title={Algorithms for association rule mining — a general survey and comparison},
  author={Jochen Hipp and Ulrich G{\"u}ntzer and Gholamreza Nakhaeizadeh},
  journal={SIGKDD Explor.},
  year={2000},
  volume={2},
  pages={58-64}
}
ABSTRACT Today there are several eAE ient algorithms that ope with the popular and omputationally expensive task of asso iation rule mining. A tually, these algorithms are more or less des ribed on their own. In this paper we explain the fundamentals of asso iation rule mining and moreover derive a general framework. Based on this we des ribe today's approa hes in ontext by pointing out ommon aspe ts and di eren es. After that we thoroughly investigate their strengths and weaknesses and arry… 

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References

SHOWING 1-10 OF 30 REFERENCES
Mining Association Rules: Deriving a Superior Algorithm by Analyzing Today's Approaches
TLDR
This paper identifies the fundamental strategies of association rule mining and presents a general framework that is independent of any particular approach and its implementation, and achieves remarkably better runtimes than the previous algorithms.
Finding interesting associations without support pruning
  • E. Cohen, Mayur Datar, Cheng Yang
  • Computer Science
    Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073)
  • 2000
TLDR
This work develops a family of algorithms for solving association rule mining, employing a combination of random sampling and hashing techniques and provides an analysis of the algorithms developed and conduct experiments on real and synthetic data to obtain a comparative performance analysis.
A New Algorithm for Faster Mining of Generalized Association Rules
TLDR
A new algorithm is derived, called Prutax, to mine generalized frequent itemsets, which is an order of magnitude faster than previous approaches to generate frequent itemset rules.
Query flocks: a generalization of association-rule mining
TLDR
The general idea, called “query flocks,” is a generate-and-test model for data-mining problems and is shown how the idea can be used either in a general-purpose mining system or in a next generation of conventional query optimizers.
Set-oriented mining for association rules in relational databases
  • M. Houtsma, A. Swami
  • Computer Science
    Proceedings of the Eleventh International Conference on Data Engineering
  • 1995
TLDR
This paper shows that at least some aspects of data mining can be carried out by using general query languages such as SQL, rather than by developing specialized black-box algorithms.
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.
Mining Association Rules with Item Constraints
TLDR
This work considers the problem of integrating constraints that are Boolean expressions over the presence or absence of items into the association discovery algorithm and presents three integrated algorithms for mining association rules with item constraints and discusses their tradeoffs.
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.
Dynamic itemset counting and implication rules for market basket data
TLDR
A new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling and a new way of generating “implication rules” which are normalized based on both the antecedent and the consequent.
...
1
2
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