Dynamic Multi-Network Mining of Tensor Time Series

arXiv:2402.11773v1 Announce Type: new
Abstract: Subsequence clustering of time series is an essential task in data mining, and interpreting the resulting clusters is also crucial since we generally do not have prior knowledge of the data. Thus, given a large collection of tensor time series consisting of multiple modes, including timestamps, how can we achieve subsequence clustering for tensor time series and provide interpretable insights? In this paper, we propose a new method, Dynamic Multi-network Mining (DMM), that converts a …

abstract arxiv clustering collection cs.ai cs.it cs.lg data data mining dynamic knowledge math.it mining multiple network prior series tensor time series type