Abstract
Weighted sequential pattern mining algorithms discover weighted sequences with considering the different significance of each item in a sequence database. But current algorithms have not considered the importance of the itemset-intervals information between the two items in a same itemset. Hence, although a large number of sequences had been discovered, most of them are not useful for analysis. In this study, we propose a new algorithm, called ItemSet-interval Weighted Sequences (ISiWS), to solve the problem about efficient discovering useful sequences. In ISiWS, a matrix structure, called Transaction Bit Matrix (TBM), represents a sequence. ISiWS first uses TBMs to represent the sequences in a sequence database. Then, it utilizes projected technology to discover weighted sequences, and an approximate sequence match algorithm is applied to calculate support of sequences based on their itemset-intervals. Experiments show that ISiWS produces a significantly less number of weighted sequences than those of WSpan.
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Fu, Y., Yu, Y., Song, M., Zhan, X. (2014). Mining Weighted Sequential Patterns in a Sequence Database with Itemset-Interval Measurement. In: Zu, Q., Vargas-Vera, M., Hu, B. (eds) Pervasive Computing and the Networked World. ICPCA/SWS 2013. Lecture Notes in Computer Science, vol 8351. Springer, Cham. https://doi.org/10.1007/978-3-319-09265-2_10
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DOI: https://doi.org/10.1007/978-3-319-09265-2_10
Publisher Name: Springer, Cham
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