Abstract
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 is not used at all for counting the support of sequences after the first pass. Rather, a tidlist structure generated in the previous pass is employed for the purpose based on set intersection operations, avoiding the multiple scans of the databases.
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