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• W12084829 abstract "Similarity measure and visualization are two of the most interesting tasks in time series data mining and attract much attention in the last decade. Some representations have been proposed to reduce high dimensionality of time series and the corresponding distance functions have been used to measure their similarity. Moreover, visualization techniques are often based on such representations. One of the most popular time series visualization is time series bitmaps using chaos-game algorithm. In this paper, we propose an alternative version of the long time series bitmaps of which the number of the alphabets is not restricted to four. Simultaneously, the corresponding distance function is also proposed to measure the similarity between long time series. Our approach transforms long time series into SAX symbolic strings and constructs a non-sparse matrix which stores the frequency of binary patterns. The matrix can be used to calculate the similarity and visualize the long time series. The experiments demonstrate that our approach not only can measure the long time series as well as the bag of (BOP), but also can obtain better visual effects of the long time series visualization than the chaos-game based time series bitmaps (CGB). Especially, the computation cost of pattern matrix construction in our approach is lower than that in CGB." @default.
• W12084829 created "2016-06-24" @default.
• W12084829 creator A5028839347 @default.
• W12084829 creator A5031118344 @default.
• W12084829 creator A5059710405 @default.
• W12084829 date "2012-01-01" @default.
• W12084829 modified "2023-09-26" @default.
• W12084829 title "A Method of Similarity Measure and Visualization for Long Time Series Using Binary Patterns" @default.
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• W12084829 doi "https://doi.org/10.1007/978-3-642-28320-8_12" @default.
• W12084829 hasPublicationYear "2012" @default.
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