Mining Recent Temporal Patterns for Event Detection in Multivariate Time Series Data.
Improving the performance of classifiers using pattern mining techniques has been an active topic of data mining research. In this work we introduce the recent temporal pattern mining framework for finding predictive patterns for monitoring and event detection problems in complex multivariate time series data. This framework first converts time series into time-interval sequences of temporal abstractions. It then constructs more complex temporal patterns backwards in time using temporal operators. We apply our framework to health care data of 13,558 diabetic patients and show its benefits by efficiently finding useful patterns for detecting and diagnosing adverse medical conditions that are associated with diabetes.
Dept. of Computer Science, University of Pittsburgh, iyad@cs.pitt.edu.
Iyad
I
Batal
Siemens Corporate Research, dmitriy.fradkin@siemens.com.
Dmitriy
D
Fradkin
Dept. of Public Health Sciences, University of Virginia, james.harrison@virginia.edu.
James
J
Harrison
Siemens Corporate Research, fabian.moerchen@siemens.com.
Fabian
F
Moerchen
Dept. of Computer Science, University of Pittsburgh, milos@cs.pitt.edu.
Milos
M
Hauskrecht
Event Detection; Patient Classification; Temporal Abstractions; Temporal Pattern Mining; Time-interval Patterns