Mining similar temporal patterns in long time-series data and its application to medicine

被引：24

作者：

Hirano, S ^{[1
]}

Tsumoto, S ^{[1
]}

机构：

[1] Shimane Med Univ, Sch Med, Dept Med Informat, Izumo, Shimane 6938501, Japan

来源：

2002 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS | 2002年

关键词：

D O I：

10.1109/ICDM.2002.1183906

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Data mining in time-series medical databases has been receiving considerable attention since it provides a way of revealing useful information hidden in the database; for example relationships between temporal course of examination results and onset time of diseases. This paper presents a new method for finding similar patterns in temporal sequences. The method is a hybridization of phase-constraint multiscale matching and rough clustering. Multiscale matching enables us cross-scale comparison of the sequences, namely, it enable us to compare temporal patterns by partially changing observation scales. Rough clustering enable us to construct interpretable clusters of the sequences even if their similarities are given as relative similarities. We combine these methods and cluster the sequences according to multiscale similarity of patterns. Experimental results on the chronic hepatitis dataset showed that clusters demonstrating interesting temporal patterns were successfully discovered.

引用

页码：219 / 226

页数：8

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