Summary
In this chapter, we propose a mining algorithm based on angles of adjacent points in a time series to find linguistic trends. The proposed approach first transforms data values into angles, and then uses a sliding window to generate continues subsequences from angular series. Several fuzzy sets for angles are predefined to represent semantic concepts understandable to human being. The a priori-like fuzzy mining algorithm is then used to generate linguistic trends. Appropriate post-processing is also performed to remove redundant patterns. Finally, experiments are made for different parameter settings, with experimental results showing that the proposed algorithm can actually work.
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Chen, CH., Hong, TP., Tseng, V.S. (2008). Mining Linguistic Trends from Time Series. In: Lin, T.Y., Xie, Y., Wasilewska, A., Liau, CJ. (eds) Data Mining: Foundations and Practice. Studies in Computational Intelligence, vol 118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78488-3_3
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DOI: https://doi.org/10.1007/978-3-540-78488-3_3
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