Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Time Series Analysis and Its Applications (Springer Texts in Statistics)
Time Series Analysis and Its Applications (Springer Texts in Statistics)
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This paper presents a method for analyzing time-series data on laboratory examinations based on phase-constraint multiscale matching and rough clustering. Multiscale matching compares two subsequences throughout various scales of view. It has an advantage of preserving connectivity of subsequences even if the subsequences are represented at different scales. Rough clustering groups up objects according not to the topographic measures such as the center or deviance of objects in a cluster but to the relative similarity and indiscernibility of objects. We use multiscale matching to obtain similarity of sequences and rough clustering to cluster the sequences according to the obtained similarity. We slightly modified dissimilarity measure in multiscale matching so that it suppresses excessive shift of phase that may cause incorrect matching of the sequences. Experimental results on the hepatitis dataset show that the proposed method successfully clustered similar sequences into an independent cluster, and that correspondence of subsequences are also successfully captured.