Mining Similar Temporal Patterns in Long Time-Series Data and Its Application to Medicine

Authors:
Shoji Hirano;Shusaku Tsumoto
Affiliations:
-;-
Venue:
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Year:
2002

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Abstract

Data mining in time-series medical databases has beenreceiving considerable attention since it provides a way ofrevealing useful information hidden in the database; forexample relationships between temporal course of examinationresults and onset time of diseases. This paperpresents a new method for finding similar patterns in temporalsequences. The method is a hybridization of phase-constraintmultiscale matching and rough clustering. Multiscalematching enables us cross-scale comparison of thesequences, namely, it enable us to compare temporal patternsby partially changing observation scales. Rough clusteringenable us to construct interpretable clusters of thesequences even if their similarities are given as relativesimilarities. We combine these methods and cluster the sequencesaccording to multiscale similarity of patterns. Experimentalresults on the chronic hepatitis dataset showedthat clusters demonstrating interesting temporal patternswere successfully discovered.