Paper Info
Title
Cluster Analysis of Time-Series Medical Data Based on the Trajectory Representation and Multiscale Comparison Techniques
Abstract
This paper presents a cluster analysis method for multidimensional time-series data on clinical laboratory examinations. Our method represents the time series of test results as trajectories in multidimensional space, and compares their structural similarity by using the multiscale comparison technique. It enables us to find the part-to-part correspondences between two trajectories, taking into account the relationships between different tests. The resultant dissimilarity can be further used with clustering algorithms for finding the groups of similar cases. The method was applied to the cluster analysis of Albumin-Platelet data in the chronic hepatitis dataset. The results denonstrated that it could form interesting groups of cases that have high correspondence to the fibrotic stages.
Year
DOI
Venue
2006
10.1109/ICDM.2006.33
Hong Kong
Keywords
Field
DocType
medical computing,pattern clustering,time series,clinical laboratory examination,cluster analysis,multidimensional space,multiscale comparison technique,time-series medical data,trajectory representation
Data mining,Computer science,Pattern clustering,Artificial intelligence,Cluster analysis,Trajectory,Machine learning
Conference
ISSN
ISBN
Citations 
1550-4786
0-7695-2701-7
12
PageRank 
References 
Authors
0.60
11
2
Name
Order
Citations
PageRank
Shoji Hirano156099.17
Shusaku Tsumoto21820294.19