Characteristics of Multivariate Time Series and Review for Clustering Methods
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초록
As technology for data storage in business has been improved, research for analyzing this complicated dataset has accounted for this matter. Data mining is one of these efforts. This study described the needs for statistical tool for clustering complicated multi‐dimensional time series and representative statistical methods for clustering analysis including raw data or model based approach, feature extraction approach, and multi‐way principal component analysis (MPCA). As the closing this review study, Kernel variant principal component analysis (KPCA) was described as the alternative method for non‐linear cases.
Abstract
As technology for data storage in business has been improved, research for analyzing this complicated dataset has accounted for this matter. Data mining is one of these efforts. This study described the needs for statistical tool for clustering complicated multi‐dimensional time series and representative statistical methods for clustering analysis including raw data or model based approach, feature extraction approach, and multi‐way principal component analysis (MPCA). As the closing this review study, Kernel variant principal component analysis (KPCA) was described as the alternative method for non‐linear cases.
참고문헌 (24)
- Agrawal, Rakesh, "Efficient Similarity Search in Sequence Databases", International Conference on Foundations of Data Organization (1993)
- Agrawal, Rakesh, "Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time‐Series Databases", 21th International Conference on Very Large Databases, Zurich, Switzerland (1995)
- Baragona, Roberto, "A Simulation Study on Clustering Time Series with Metaheusitic Methods", Quaderni di Statistica, 3, pp.1~26 (2001)
- Box, George Edward P., "Time Series Analysis: Forecasting and Control. 2nd edition" (1976), Holden‐Day
- Chan, Kin‐Pong, "Efficient Time Series Matching by Wavelets", 15th IEEE International Conference on Data Engineering, Sydney, Australia, pp.126~133 (1999)
- Enders, Walter, "Applied Econometric Time Series. 1st ed" (1995), John Wiley & Sons, Inc
- Flury, Bernhard, "Common Principal Components and Related Multivariate Models" (1988), New York: John Wiley
- Johnson, Richard A., "Applied Multivariate Statistical Analysis. 5th ed" (2002), New Jersey: Prentice Hall
- Kakizawa, Yoshihide, "Discrimination and Clustering for Multivariate Time Series", Journal of the American Statistical Association, 93, pp.328~340 (1998)
- Kapteyn, Arie, "An Approach to N‐Mode Components Analysis", Psychometrika, 51, pp.269~275 (1986)
- Korn, Flip, "Efficiently Supporting Ad Hoc Queries in Large Datasets of Time Sequences", ACM SIGMOD International Conference on Management of Data, pp.289~300 (1997), ACM, Inc.
- Kosanovich, Karlene A., "Multi‐Way PCA Applied to an Industrial Batch Process", American Control Conference, Baltimore, MD, pp.1294~1298 (1994)
- Kosmelj, Katarina, "Cross‐Sectional Approach for Clustering Time Varying Data", Journal of Classification, 7, pp.99~109 (1990)
- Liao, T. Warren, "Clustering of Time Series Data‐a Survey", Pattern Recognition, 38, pp.1857~1874 (2005)
- Maharaj, E. Ann, "Clusters of Time Series", Journal of Classification, 17, pp.297~314 (2000)
- Muller, Klaus‐Robert, "An Introduction to Kernel‐Based Learning Algorithms", IEEE Transactions on Neural Networks, 12, pp.181~201 (2001)
- Oates, Tim, "Clustering Time Series with Hidden Markov Models and Dynamic Time Warping", Proceedings of the IJCAI‐99 Workshop on Neural, Symbolic, and Reinforcement Learning Methods for Sequence Learning (1999)
- Ramoni, Marco, "Multivariate Clustering by Dynamics", Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence (2000)
- Ramoni, Marco, "Bayesian Clustering by Dynamics", Machine Learning, pp.1~31 (2001)
- Sholkopf, Bernhard, "Learning with Kernels ‐Support Vector Machines, Regularization, Optimization, and Beyond" (2002), Massachusetts: The MIT Press
- Shumway, Robert H., "Applied Statistical Time Series Analysis" (1988), New Jersey: Prentice Hall
- Toshniwal, Durga, "Finding Similarity in Time Series Data by Method of Time Weighted Moments", Australasian Database Conference (ADC 2005) (2005)
- Wold, Svante, "Principal Component Analysis", Chemometrics and Intelligent Laboratory Systems, 2, pp.37~52 (1987)
- Wold, Svante, "Multi‐Way Principal Components and PLS‐Analysis", Journal of Chemometrics, 1, pp.41~56 (1987)