Automated discovery of chronological patterns in long time-series medical datasets

Data mining in time-series medical databases has been receiving considerable attention because it provides a way of revealing useful information hidden in the database, for example, relationships between the temporal course of examination results and the onset time of diseases. This article 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 to cross-scale a comparison of the sequences, namely, it enables us to compare temporal patterns by partially changing observation scales. Rough clustering enables 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 the multiscale similarity of patterns. Experimental results on the chronic hepatitis dataset showed that clusters demonstrating interesting temporal patterns were successfully discovered. (c) 2005 Wiley Periodicals, Inc.