The data mining and machine learning communities were surprised when Keogh et al. (2003) pointed out that the k-means cluster centers in subsequence time-series clustering become sinusoidal pseudopatterns for almost all kinds of input time-series data. Understanding this mechanism is an important open problem in data mining. Our new theoretical approach (based on spectral clustering and translational symmetry) explains why the cluster centers of k-means naturally tend to form sinusoidal patterns. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Idé, T. (2006). Why does subsequence time-series clustering produce sine waves? In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4213 LNAI, pp. 211–222). Springer Verlag. https://doi.org/10.1007/11871637_23
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