2009 Fiscal Year Final Research Report

Research on automatic grouping of multivariate time series based on the information granularity

Research Project

Project/Area Number	20700140
Research Category	Grant-in-Aid for Young Scientists (B)
Allocation Type	Single-year Grants
Research Field	Intelligent informatics
Research Institution	Shimane University
Principal Investigator	HIRANO Shoji Shimane University, 医学部, 准教授 (60333506)
Project Period (FY)	2008 – 2009
Keywords	データマイニング / 知識工学 / 時系列 / 情報システム
Research Abstract	In this research we have developed a multiscale comparison method for multivariate time series. Our method firstly constructs multidimensional trajectories from the time series, and represent them using multiscale representation. Next, it splits the trajectories into data granules according to the positions of curvature maxima. Then it traces the hierarchical structure of data granules and performs granule-by-granule matching across the scales to find the best correspondences between the trajectories. Experimental results on a medical dataset showed that our method could generate groups of trajectories that exhibited similar temporal courses, and some of the clusters showed interesting characteristics about the distribution of fibrotic stages.

(2 results)