Abstract
Sequential pattern mining is to find frequent data sequences with time. When sequential patterns are generated, the newly arriving patterns may not be identified as frequent sequential patterns due to the existence of old data and sequences. Progressive sequential pattern mining aims to find most up-to-date sequential patterns given that obsolete items will be deleted from the sequences. When sequences come with multiple data streams, it is difficult to maintain and update the current sequential patterns. Even worse, when we consider the sequences across multiple streams, previous methods could not efficiently compute the frequent sequential patterns. In this work, we propose an efficient algorithm PAMS to address this problem. PAMS uses a PSM-tree to insert new items, update current items, and delete obsolete items. The experimental results show that PAMS significantly outperforms previous algorithms for mining progressive sequential patterns across multiple streams.
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Jaysawal, B.P., Huang, JW. (2014). Mining Frequent Progressive Usage Patterns Across Multiple Mobile Broadcasting Channels. In: Peng, WC., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8643. Springer, Cham. https://doi.org/10.1007/978-3-319-13186-3_14
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DOI: https://doi.org/10.1007/978-3-319-13186-3_14
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