Name
Title | ||
|---|---|---|
Mining Recent Temporal Patterns for Event Detection in Multivariate Time Series Data. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2339530.2339578 | KDD |
Keywords | Field | DocType |
event detection,temporal abstractions,complex temporal pattern,converts time series,predictive pattern,complex multivariate time series,patient classification,temporal abstraction,health care data,recent temporal pattern mining,multivariate time series data,temporal operator,time-interval patterns,pattern mining technique,data mining research,temporal pattern mining,health care,data mining,time series | Health care,Data mining,Time series,Text mining,Computer science,Multivariate statistics,Temporal pattern mining,Operator (computer programming),Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
2012 | 2154-817X | 22 |
PageRank | References | Authors |
0.92 | 28 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Iyad Batal | 1 | 192 | 10.61 |
| Dmitriy Fradkin | 2 | 344 | 19.25 |
| James Harrison | 3 | 22 | 0.92 |
| Fabian Moerchen | 4 | 172 | 10.51 |
| Milos Hauskrecht | 5 | 921 | 90.70 |