Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Depth first generation of long patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Computing Association Rules Using Partial Totals
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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A key stage in the discovery of Association Rules in binary databases involves the identification of the "frequent sets", i.e. those sets of attributes that occur together often enough to invite further attention. This stage is also the most computationally demanding, because of the exponential scale of the search space. Particular difficulty is encountered in dealing with very densely-populated data. A special case of this is that of, for example, demographic or epidemiological data, which includes some attributes with very frequent instances, because large numbers of sets involving these attributes will need to be considered. In this paper we describe methods to address this problem, using methods and heuristics applied to a previously-presented generic algorithm, Apriori-TFP. The results we present demonstrate significant performance improvements over the original Apriori-TFP in datasets which include subsets of very frequently-occurring attributes.