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
Parallel Algorithms for Discovery of Association Rules
Data Mining and Knowledge Discovery
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
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
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Mining sequential patterns from large databases has been recognized by many researchers as an attractive task of data mining and knowledge discovery. Previous algorithms scan the databases for many times, which is often unendurable due to the very large amount of databases. In this paper, the authors introduce an effective algorithm for mining sequential patterns from large databases. In the algorithm, the original database are not used at all for counting the support of sequences after the first pass. Rather, a tid list structure generated in the previous pass is employed for the purpose based on set intersection operations, avoiding the multiple scans of the databases.