Abstract
Being one of the most useful fields of data mining, sequential pattern mining is a very popular and much researched domain. However, simply pattern mining is often not enough to understand the intricate relationships that exist between data objects or items. A correlation measure can uplift the task of mining interesting information that is useful to the end user. In this paper, we propose a new correlation measure, SequentialCorrelation, for sequential patterns. Along with that, we propose a complete method called SCMine and design its efficient trie-based implementation. We use the measure to define a one or two way relationship between data objects and subsequently classify patterns into two subsets based on order dependency. Our performance study shows that a number of insignificant patterns can be pruned and it can give valuable insight into the datasets. SequentialCorrelation along with SCMine can be very useful in many real life applications, especially because conventional correlation measures are not applicable in sequential datasets.
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Arefin, M.F., Islam, M.T., Ahmed, C.F. (2018). Mining Sequential Correlation with a New Measure. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2018. Lecture Notes in Computer Science(), vol 10933. Springer, Cham. https://doi.org/10.1007/978-3-319-95786-9_3
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DOI: https://doi.org/10.1007/978-3-319-95786-9_3
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