ROCK: a robust clustering algorithm for categorical attributes
Information Systems
Automatic clustering of vector time-series for manufacturing machine monitoring
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
A PCA-based similarity measure for multivariate time series
Proceedings of the 2nd ACM international workshop on Multimedia databases
Clustering Time Series with Clipped Data
Machine Learning
Pattern recognition in time series database: A case study on financial database
Expert Systems with Applications: An International Journal
Distance functions for categorical and mixed variables
Pattern Recognition Letters
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The variables of multivariate time series (MTS) can be numeric or categorical attribute, but many researches payed attention to numeric attribute. This paper focuses on MTS with mixed attributes. A novel approach of weighted matrix coverage is proposed to judge the neighborhood between MTS based on Singular Value Decomposition (SVD) and a notion about the number of common neighbors (NCN) is introduced to measure the similarities. In turn, a modified hierarchical clustering algorithm is put forward. The experimental results show that our algorithm performs better than the standard hierarchical clustering algorithm based on Dynamic Time Wrapping (DTW) distance metric.