Further Pruning for Efficient Association Rule Discovery
نویسندگان
چکیده
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%.
منابع مشابه
Rule Pruning in Associative Classification Mining
Classification and association rule discovery are important data mining tasks. Using association rule discovery to construct classification systems, also known as associative classification, is a promising approach. In this paper, we survey different rule pruning methods used by associative classification techniques. Furthermore, we compare the effect of three pruning methods (database coverage...
متن کاملEffective Pruning for the Discovery of Conditional Functional Dependencies
Conditional Functional Dependencies (CFDs) have been proposed as a new type of semantic rules extended from traditional functional dependencies. They have shown great potential for detecting and repairing inconsistent data. Constant CFDs are 100% confidence association rules. The theoretical search space for the minimal set of CFDs is the set of minimal generators and their closures in data. Th...
متن کاملPruning Techniques in Associative Classification: Survey and Comparison
Association rule discovery and classification are common data mining tasks. Integrating association rule and classification also known as associative classification is a promising approach that derives classifiers highly competitive with regards to accuracy to that of traditional classification approaches such as rule induction and decision trees. However, the size of the classifiers generated ...
متن کاملRegional Association Rule Mining
This project [4] centers on regional association rule mining and scoping in spatial datasets. We introduces a methodology for mining spatial association rules and proposes new algorithms to determine the scope of a spatial association rule. We develop a reward-based region discovery framework that employs clustering to find interesting regions. The framework is applied to solve two distinct reg...
متن کاملOn pruning strategies for discovery of generalized and quantitative association rules
Mining association rules has become an important datamining task, and meanwhile many algorithms have been developed which often differ in several aspects. In this paper, we analyse and compare the pruning strategies of several algorithms that were designed for mining generalised and quantitative association rules while abstracting from other technical details. Furthermore, we sketch a novel pru...
متن کامل