Abstract
The technique of classification can sort data into various categories for data mining studies. The demand for sequence data classification has increased with the development of information technology. Several applications involve decision prediction based on sequence data, but the traditional classification methods are unsuitable for sequence data. Thus, this paper proposes a Pattern Coverage Rate-based Sequence Classification Model (PCRSCM) to integrate sequential pattern mining and classification techniques. PCRSCM mines sequential patterns to find characteristics of each class, and then calculates pattern coverage rates and class scores to predict the class of a sequence. The experimental results show that PCRSCM exhibits excellent prediction performance on synthetic and real sequence data.
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Li, IH., Huang, JY., Liao, IE., Lin, JH. (2013). A Sequence Classification Model Based on Pattern Coverage Rate. In: Park, J.J.(.H., Arabnia, H.R., Kim, C., Shi, W., Gil, JM. (eds) Grid and Pervasive Computing. GPC 2013. Lecture Notes in Computer Science, vol 7861. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38027-3_81
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DOI: https://doi.org/10.1007/978-3-642-38027-3_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38026-6
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