Abstract
The paper deals with the search and analysis of the subsequences in large volume sequences (texts, DNA sequences, etc.). A new algorithm ProMFS for mining frequent sequences is proposed and investigated. It is based on the estimated probabilistic-statistical characteristics of the appearance of elements of the sequence and their order. The algorithm builds a new much shorter sequence and makes decisions on the main sequence in accordance with the results of analysis of the shorter one.
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Tumasonis, R., Dzemyda, G. (2005). Analysis of the Statistical Characteristics in Mining of Frequent Sequences. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32392-9_39
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DOI: https://doi.org/10.1007/3-540-32392-9_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25056-2
Online ISBN: 978-3-540-32392-1
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