Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

KDD : Proceedings
David HallacJure Leskovec

Abstract

Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We deri...Continue Reading

Citations

Jul 31, 2020·Data Mining and Knowledge Discovery·Wei ZhangJr-Shin Li
Jul 26, 2019··Yasushi SakuraiYasuko Matsubara
Nov 4, 2019··Yasuko MatsubaraYasushi Sakurai
May 1, 2019··Jason BlumFrancesco Masillo
Aug 20, 2020··Koki KawabataTakato Honda
May 22, 2018··Yasushi SakuraiKouki Kawabata

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