Distance Measures for Effective Clustering of ARIMA Time-Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Pattern Extraction for Time Series Classification
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Mixtures of ARMA Models for Model-Based Time Series Clustering
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
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SB-Tree is a data structure proposed to represent time series according to the importance of the data points. Its advantages over traditional time series representation approaches include: representing time series directly in time domain (shape preservation), retrieving time series data according to the importance of the data points and facilitating multi-resolution time series retrieval. Based on these benefits, one may find this representation particularly attractive in financial time series domain and the corresponding data mining tasks, i.e. categorization and clustering. In this paper, an investigation on the size of the SB-Tree is reported. Two SB-Tree optimization approaches are proposed to reduce the size of the SB-Tree while the overall shape of the time series can be preserved. As demonstrated by various experiments, the proposed approach is suitable for different categorization and clustering applications.