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

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.