Abstract
In this paper, a novelty methodology for the representation and similarity measurement of sequential data is presented. First, a linear segmentation algorithm based on feature points is proposed. Then, two similarity measures are defined from the differences between the behavior and the mean level of the sequential data. These similarities are calculated for clustering and outlier detection of subjective sequential data generated through the evaluation of the driving risk obtained from a group of traffic safety experts. Finally, a novel dissimilarity measure for outlier detection of paired sequential data is proposed. The results of the experiments show that both similarities contain complementary and relevant information about the dataset. The methodology results useful to find patterns on subjective data related with the behavior and the level of the data.
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Siordia, O.S., de Diego, I.M., Conde, C., Cabello, E. (2011). Section-Wise Similarities for Clustering and Outlier Detection of Subjective Sequential Data. In: Pelillo, M., Hancock, E.R. (eds) Similarity-Based Pattern Recognition. SIMBAD 2011. Lecture Notes in Computer Science, vol 7005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24471-1_5
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DOI: https://doi.org/10.1007/978-3-642-24471-1_5
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