Constraint-based rule mining in large, dense databases

Bayardo, Roberto J.; Agrawal, Rakesh; Gunopulos, Dimitrios

doi:10.1109/icde.1999.754924

Cited by 299 publications

(264 citation statements)

References 8 publications

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“…CBA [18] uses pessimistic error pruning. Another possibility is to simply use some measure of improvement [5] on a chosen rule quality metric. Using the same example as above, if we set a minimal confidence improvement of 0.1, we may discard {a, b, c} → g if its confidence is less than conf idence({a, c} → g) + 0.1.…”

Section: Rule Selectionmentioning

confidence: 99%

An Experiment with Association Rules and Classification: Post-Bagging and Conviction

Jorge

Azevedo

2005

Discovery Science

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Abstract. In this paper we study a new technique we call post-bagging, which consists in resampling parts of a classification model rather then the data. We do this with a particular kind of model: large sets of classification association rules, and in combination with ordinary best rule and weighted voting approaches. We empirically evaluate the effects of the technique in terms of classification accuracy. We also discuss the predictive power of different metrics used for association rule mining, such as confidence, lift, conviction and χ 2 . We conclude that, for the described experimental conditions, post-bagging improves classification results and that the best metric is conviction.

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Section: Rule Selectionmentioning

confidence: 99%

An Experiment with Association Rules and Classification: Post-Bagging and Conviction

Jorge

Azevedo

2005

Discovery Science

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“…This extends previous work in rule discovery search [5,8,10,12,13,14,15] by searching for rules that optimize an objective function over a space of rules that allows alternative variables in the consequent. Previous algorithms have all been restricted to a single target consequent variable per search.…”

Section: Introductionmentioning

confidence: 55%

Further Pruning for Efficient Association Rule Discovery

Zhang

Webb

2001

AI 2001: Advances in Artificial Intelligence

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Abstract. The Apriori algorithm's frequent itemset approach has become the standard approach to discovering association rules. However, the computation requirements of the frequent itemset approach are infeasible for dense data and the approach is unable to discover infrequent associations. OPUS AR is an efficient algorithm for association rule discovery that does not utilize frequent itemsets and hence avoids these problems. It can reduce search time by using additional constraints on the search space as well as constraints on itemset frequency. However, the effectiveness of the pruning rules used during search will determine the efficiency of its search. This paper presents and analyses pruning rules for use with OPUS AR. We demonstrate that application of OPUS AR is feasible for a number of datasets for which application of the frequent itemset approach is infeasible and that the new pruning rules can reduce compute time by more than 40%.

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“…By using this technique we perform significance tests among rules and discard those happen to appear interesting only by chance. To provide a clear idea of insignificant rules, we will at first introduce the concept of rule improvement defined by Bayardo et al [5]. Confidence improvement which is used as an example, defined a minimum improvement in confidence that a propositional rule must exhibit in order to be regarded as potentially interesting:…”

Section: Improvementmentioning

confidence: 99%

Efficiently Identifying Exploratory Rules’ Significance

Huang

Webb

2006

Lecture Notes in Computer Science

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Abstract. How to efficiently discard potentially uninteresting rules in exploratory rule discovery is one of the important research foci in data mining. Many researchers have presented algorithms to automatically remove potentially uninteresting rules utilizing background knowledge and user-specified constraints. Identifying the significance of exploratory rules using a significance test is desirable for removing rules that may appear interesting by chance, hence providing the users with a more compact set of resulting rules. However, applying statistical tests to identify significant rules requires considerable computation and data access in order to obtain the necessary statistics. The situation gets worse as the size of the database increases. In this paper, we propose two approaches for improving the efficiency of significant exploratory rule discovery. We also evaluate the experimental effect in impact rule discovery which is suitable for discovering exploratory rules in very large, dense databases.

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Constraint-based rule mining in large, dense databases

Cited by 299 publications

References 8 publications

An Experiment with Association Rules and Classification: Post-Bagging and Conviction

An Experiment with Association Rules and Classification: Post-Bagging and Conviction

Further Pruning for Efficient Association Rule Discovery

Efficiently Identifying Exploratory Rules’ Significance

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