Classification of temporal sequences using multiscale matching and rough clustering
Shoji Hirano, Shusaku Tsumoto · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
This paper presents a novel method for clustering time-series medical data based on the improved multiscale matching. Multiscale matching, developed originally as a pattern recognition technique, has an ability to compare two shapes by partly changing observation scales. We have made some improvements to the conventional multiscale matching in order to enable the cross-scale, granularity-based comparison of long-term time-series sequences. The key idea is development of a new segment representation that eludes the problem of shrinkage. We induced shape parameters of a segment at high scale directly from the base segments at the lowest scale, instead of using shapes represented by multiscale description. We examined the usefulness of the method on the cylinder-bell-funnel dataset and chronic hepatitis dataset. The results demonstrated that the dissimilarity matrix produced by the proposed method, conbined with conventional clustering techniques, lead to the successful clustering for both synthetic and real-world data.