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AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering. (arXiv:2208.04313v2 [cs.LG] UPDATED)
Aug. 19, 2022, 1:11 a.m. | Guozhong Li, Byron Choi, Jianliang Xu, Sourav S Bhowmick, Daphne Ngar-yin Mah, Grace Lai-Hung Wong
cs.LG updates on arXiv.org arxiv.org
Time series shapelets are discriminative subsequences that have been recently
found effective for time series clustering (TSC). The shapelets are convenient
for interpreting the clusters. Thus, the main challenge for TSC is to discover
high-quality variable-length shapelets to discriminate different clusters. In
this paper, we propose a novel autoencoder-shapelet approach (AUTOSHAPE), which
is the first study to take the advantage of both autoencoder and shapelet for
determining shapelets in an unsupervised manner. An autoencoder is specially
designed to learn high-quality …
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