Abstract
Mining of sequential patterns is an important issue among the various data mining problems. The problem of incremental mining of sequential patterns deserves as much attention. In this paper, we consider the problem of the incremental updating of sequential pattern mining when some transactions and/or data sequences are deleted from the original sequence database. We present a new algorithm, called IU_D, for mining frequent sequences so as to make full use of information obtained during an earlier mining process for reducing the cost of finding new sequential patterns in the updated database. The results of our experiment show that the algorithm performs significantly faster than the naive approach of mining the entire updated database from scratch.
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Ren, JD., Zhou, XL. (2006). An Efficient Algorithm for Incremental Mining of Sequential Patterns. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_19
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DOI: https://doi.org/10.1007/11739685_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
Online ISBN: 978-3-540-33585-6
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