Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Mining Similar Temporal Patterns in Long Time-Series Data and Its Application to Medicine
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Cluster Analysis
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Journal of Intelligent Information Systems
Active mining project: overview
AM'03 Proceedings of the Second international conference on Active Mining
Feature selection for classification of oscillating time series
Expert Systems: The Journal of Knowledge Engineering
Stock market co-movement assessment using a three-phase clustering method
Expert Systems with Applications: An International Journal
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In this paper we report some characteristics of time-series comparison methods and clustering methods found empirically using a real-world medical database. First, we examined basic characteristics of two sequence comparison methods, multiscale matching (MSM) and dynamic time warping (DTW), using a simple sine wave and its variants. Next, we examined the characteristics of various combinations of sequence comparison methods and clustering methods, in terms of interpretability of generating clusters, using a time-series medical database. Although the subjects for comparison were limited, the results demonstrated that (1) shape representation parameters in MSM could capture the structural feature of time series; for example, the difference of amplitude was successfully captured using rotation term, and that differences on phases and trends were also successfully reflected in the dissimilarity. (2) However, the dissimilarity induced by MSM lacks linearity compared with DTW. It was also demonstrated that (1) complete-linkage criterion (CL-AHC) outperforms average-linkage (AL-AHC) criterion in terms of the interpret-ability of a dendrogram and clustering results, (2) combination of DTW and CL-AHC constantly produced interpretable results, (3) combination of DTW and RC would be used to find core sequences of the clusters. MSM may suffer from the problem of 'no-match' pairs, however, the problem may be eluded by using RC as a subsequent grouping method.