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
Computing Iceberg Queries Efficiently
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
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
Finding Interesting Associations without Support Pruning
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
ACE: exploiting correlation for energy-efficient and continuous context sensing
Proceedings of the 10th international conference on Mobile systems, applications, and services
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The standard model for association-rule mining involves a set of "items" and a set of "baskets." The baskets contain items that some customer has purchased at the same time. The problem is to find pairs, or perhaps larger sets, of items that frequently appear together in baskets. We mention the principal approaches to eefficient, large-scale discovery of the frequent itemsets, including the a-priori algorithm, improvements using hashing, and one- and two-pass probabilistic algorithms for finding frequent itemsets. We then turn to techniques for finding highly correlated, but infrequent, pairs of items. These notes were written for CS345 at Stanford University and are reprinted by permission of the author. http://www-db.stanford.edu/~ullman/mining/mining.html gives you access to the entire set of notes, including additional citations and on-line links.