Clustering time series from ARMA models with clipped data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering Time Series with Clipped Data
Machine Learning
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ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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IEEE Transactions on Intelligent Transportation Systems
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FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
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Clustering problems are central to many knowledge discoveryand data mining tasks. However, most existing clusteringmethods can only work with fixed-dimensional representationsof data patterns. In this paper, we study the clusteringof data patterns that are represented as sequencesor time series possibly of different lengths. We propose amodel-based approach to this problem using mixtures of autoregressivemoving average (ARMA) models. We derive anexpectation-maximization (EM) algorithm for learning themixing coefficients as well as the parameters of the componentmodels. Experiments were conducted on simulatedand real datasets. Results show that our method comparesfavorably with another method recently proposed by othersfor similar time series clustering problems.