Paper in IEEE Data Mining Conference 2007 on “Detecting Subdimensional Motifs: An Efficient Algorithm for Generalized Multivariate Pattern Discovery”

Citation / Paper

Abstract

Discovering recurring patterns in time series data is a fundamental problem for temporal data mining. This paper addresses the problem of locating sub-dimensional motifs in real-valued, multivariate time series, which requires the simultaneous discovery of sets of recurring patterns along with the corresponding relevant dimensions. While many approaches to motif discovery have been developed, most are restricted to categorical data, univariate time series, or multivariate data in which the temporal patterns span all dimensions. In this paper, we present an expected linear-time algorithm that addresses a generalization of multivariate pattern discovery in which each motif may span only a subset of the dimensions. To validate our algorithm, we discuss its theoretical properties and empirically evaluate it using several data sets, including synthetic data and motion capture data collected by an on-body inertial sensor.

Links

• https://doi.org/10.1109/ICDM.2007.52
• doi:10.1109/ICDM.2007.52

BibTeX (Download)

@inproceedings{2007-Minnen-DSMEAGMPD,
title = {Detecting Subdimensional Motifs: An Efficient Algorithm for Generalized Multivariate Pattern Discovery},
author = {D. Minnen and I. Essa and C. Isbell and T. Starner},
url = {https://doi.org/10.1109/ICDM.2007.52},
doi = {10.1109/ICDM.2007.52},
year  = {2007},
date = {2007-10-01},
urldate = {2007-10-01},
booktitle = {IEEE International Conference on Data Mining (ICDM)},
abstract = {Discovering recurring patterns in time series data is a fundamental problem for temporal data mining. This paper addresses the problem of locating sub-dimensional motifs in real-valued, multivariate time series, which requires the simultaneous discovery of sets of recurring patterns along with the corresponding relevant dimensions. While many approaches to motif discovery have been developed, most are restricted to categorical data, univariate time series, or multivariate data in which the temporal patterns span all dimensions. In this paper, we present an expected linear-time algorithm that addresses a generalization of multivariate pattern discovery in which each motif may span only a subset of the dimensions. To validate our algorithm, we discuss its theoretical properties and empirically evaluate it using several data sets, including synthetic data and motion capture data collected by an on-body inertial sensor.
},
keywords = {activity recognition},
pubstate = {published},
tppubtype = {inproceedings}
}