Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Scalable Techniques for Mining Causal Structures
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th 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
Mining Generalized Association Rules
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
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An important issue that needs to be addressed when using association rules is the validity of the rules for new situations. Rules are typically derived from the patterns in a particular dataset. When the conditions under which the dataset has been obtained change, a new situation is said to have risen. Since the conditions existing at the time of observation could affect the observed data, a change in those conditions could imply a changed set of rules for a new situation. Using the set of rules derived from the dataset for an earlier situation could lead to wrong decisions. In this paper, we provide a model explaining the difference between the sets of rules for different situations. Our model is based on the concept of rule-generating groups that we call caucuses. Using this model, we provide a simple technique, called Linear Combinations, to get a good estimate of the set of rules for a new situation. Our approach is independent of the core mining process, and so can be easily implemented with any specific technique for association rule mining. In our experiments using controlled datasets, we found that we could get up to 98.3% accuracy with our techniques as opposed to 26.6% when directly using the results of the old situation.