Active and Dynamic Approaches for Clustering Time Dependent Information: Lag Target Time Series Clustering and Multi-Factor Time Series Clustering
One of data mining schemes in statistics is clustering panel data such as longitudinal data and time series data. Classical approaches to cluster such time dependent information do not properly count time dependencies among objects we are interested to analyze. In the present study, we propose an ap...
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| Published in | Journal of statistical theory and applications Vol. 17; no. 3; pp. 462 - 477 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.09.2018
Springer Nature B.V Springer |
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| Abstract | One of data mining schemes in statistics is clustering panel data such as longitudinal data and time series data. Classical approaches to cluster such time dependent information do not properly count time dependencies among objects we are interested to analyze. In the present study, we propose an approach which takes time dependencies into our consideration by introducing appropriate weight factors with an add-on approach which allows us to measure pairwise distances in multi-dimensional space not just in two dimension. We refer to these approaches
LTTC (Lag Target Time Series Clustering)
and
MFTC (Multi-Factor Time Series Clustering)
, respectively. These proposed methods in the present study are applicable to any time dependent information from various research areas, and we have applied these methods to state level brain cancer mortality rates in the United States that illustrates the importance of subject methods. |
|---|---|
| AbstractList | One of data mining schemes in statistics is clustering panel data such as longitudinal data and time series data. Classical approaches to cluster such time dependent information do not properly count time dependencies among objects we are interested to analyze. In the present study, we propose an approach which takes time dependencies into our consideration by introducing appropriate weight factors with an add-on approach which allows us to measure pairwise distances in multi-dimensional space not just in two dimension. We refer to these approaches LTTC (Lag Target Time Series Clustering) and MFTC (Multi-Factor Time Series Clustering), respectively. These proposed methods in the present study are applicable to any time dependent information from various research areas, and we have applied these methods to state level brain cancer mortality rates in the United States that illustrates the importance of subject methods. One of data mining schemes in statistics is clustering panel data such as longitudinal data and time series data. Classical approaches to cluster such time dependent information do not properly count time dependencies among objects we are interested to analyze. In the present study, we propose an approach which takes time dependencies into our consideration by introducing appropriate weight factors with an add-on approach which allows us to measure pairwise distances in multi-dimensional space not just in two dimension. We refer to these approaches LTTC (Lag Target Time Series Clustering) and MFTC (Multi-Factor Time Series Clustering) , respectively. These proposed methods in the present study are applicable to any time dependent information from various research areas, and we have applied these methods to state level brain cancer mortality rates in the United States that illustrates the importance of subject methods. |
| Author | Kim, Doo Young Tsokos, Chris P. |
| Author_xml | – sequence: 1 givenname: Doo Young surname: Kim fullname: Kim, Doo Young email: dkim@shsu.edu organization: Department of Mathematics and Statistics, Sam Houston State University – sequence: 2 givenname: Chris P. surname: Tsokos fullname: Tsokos, Chris P. organization: Department of Mathematics and Statistics, University of South Florida |
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| Copyright | the Authors 2018 the Authors 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| DOI | 10.2991/jsta.2018.17.3.5 |
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| References | C. Hervada-Sala and E. Jarauta-Bragulat, A Program to Perform Ward’s Clustering Method on Several Regionalized Variables, Computers & Geosciences, 30 (8) (2004) pp. 881–886. R. De Maesschalck, D. Jouan-Rimbaud, and D. L. Massart, The Mahalanobis Distance, Chemometrics and Intelligent Laboratory Systems, 50 (1) (2000) pp. 1–18. N. Breslow, A generalized Kruskal-Wallis test for comparing K samples subject to unequal patterns of censorship, Biometrika, 57 (3) (1970) pp. 579–594. X. Wang and R. Hyndman, Characteristic-Based Clustering for Time Series Data, Data Mining and Knowledge Discovery, 13 (2006) pp. 335–364. S. Hayashi, Y. Tanaka, and E. Kodama, A New Manufacturing Control System Using Mahalanobis Distance for Maximising Productivity, Semiconductor Manufacturing Symposium (2001) pp. 59–62. T. Warren Liao, Clustering of time series data: A survey, Pattern Recognition, 38 (11) (2005) pp. 1857–1874. S. Deorah, C. F. Lynch, Z. A. Sibenaller, and T. C. Ryken, Trends in brain cancer incidence and survival in the United States: Surveillance, Epidemiology, and End Results Program, 1973 to 2001, Neurosurgical Focus, 20 (4) (2006) pp. E1. M. A. Smith, B. Freidlin, L. A. G. Ries, and R. Simon, Trends in Reported Incidence of Primary Malignant Brain Tumors in Children in the United States, Journal of the National Cancer Institute, 90 (17) (1998) pp. 1269–1277. A. El-Hamdouchi and P. Willett, Hierarchic Document Classification Using Ward’s Clustering Method, Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval (1986) pp. 149–156. K. Kalpakis, D. Gada, and V. Puttagunta, Distance Measures for Effective Clustering of ARIMA Time Series, Data Mining, (ICDM2001) (2001) pp. 273–280. W. H. Kruskal and W. A. Wallis, Use of ranks in one-criterion variance analysis, Journal of the American Statistical Association, 47 (260) (1952) pp. 583–621. Y. Xiong, Mixtures of ARMA models for model-based time series clustering, Data Mining, ICDM proceedings, (2002) pp. 717–720. A. M. Alonso, J.R. Berrendero, A. Hernandez, and A. Justel, Time Series Clustering Based on Forecast Densities, Computational Statistics & Data Analysis, 51 (2) (2006) pp. 762–776. G. J. Szekely, Hierarchical clustering via joint between-within distances: Extending ward’s minimum variance method, Journal of Classification, 22 (2005) pp. 151–183. P. C. Mahalanobis, On the generalized distance in statistics, Proceedings of the National Institute of Sciences (Calcutta), 2 (1936) pp. 49–55. Y. Xiong and D. Yeung, Time Series Clustering with ARMA Mixtures, Pattern Recognition, 37 (8) (2004) pp. 1675–1689. T. A. Dolecek, J. M. Propp, N. E. Stroup, and C. Kruchko, CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2005?2009, Neuro-Oncology, 14 (5) (2012) pp. v1–v49. M. Corduas and D. Piccolo, Time Series Clustering and Classification by the Autoregressive Metric, Computational Statistics & Data Analysis, 52 (4) (2008) pp. 1860–1872. C. Goutte, P. Toft, E. Rostrup, F. Nielsen, and L. Hansen, On Clustering fMRI Time Series, NeuroImage, 9 (3) (1999) pp. 298–310. E. J. Keogh and M. J. Pazzani, An enhanced Representation of Time Series which Allows Fast and Accurate Classification, Clustering and Relevance Feedback, KDD-98 Proceedings, (1998) pp. 239–278. E.Theodorsson-Norheim, Kruskal-Wallis Test: BASIC Computer Program to Perform Nonparametric One-way Analysis of Variance and Multiple Comparisons on Ranks of Several Independent Samples, Computer Methods and Programs in Biomedicine, 23 (1) (1986) pp. 57–62. D. Jiang, J, Pei, and A. Zhang, DHC: A Density-based Hierarchical Clustering Method for Time Series Gene Expression Data, Bioinformatics and Bioengineering, Proceedings, (2003) pp. 393–400. Y. Kakizawa, R. H. Shumway, and M. Taniguchi, Discrimination and Clustering for Multivariate Time Series, Journal of the American Statistical Association, 93 (441) (1998) pp. 328–340. |
| References_xml | – reference: N. Breslow, A generalized Kruskal-Wallis test for comparing K samples subject to unequal patterns of censorship, Biometrika, 57 (3) (1970) pp. 579–594. – reference: A. M. Alonso, J.R. Berrendero, A. Hernandez, and A. Justel, Time Series Clustering Based on Forecast Densities, Computational Statistics & Data Analysis, 51 (2) (2006) pp. 762–776. – reference: D. Jiang, J, Pei, and A. Zhang, DHC: A Density-based Hierarchical Clustering Method for Time Series Gene Expression Data, Bioinformatics and Bioengineering, Proceedings, (2003) pp. 393–400. – reference: A. El-Hamdouchi and P. Willett, Hierarchic Document Classification Using Ward’s Clustering Method, Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval (1986) pp. 149–156. – reference: S. Hayashi, Y. Tanaka, and E. Kodama, A New Manufacturing Control System Using Mahalanobis Distance for Maximising Productivity, Semiconductor Manufacturing Symposium (2001) pp. 59–62. – reference: Y. Kakizawa, R. H. Shumway, and M. Taniguchi, Discrimination and Clustering for Multivariate Time Series, Journal of the American Statistical Association, 93 (441) (1998) pp. 328–340. – reference: C. Goutte, P. Toft, E. Rostrup, F. Nielsen, and L. Hansen, On Clustering fMRI Time Series, NeuroImage, 9 (3) (1999) pp. 298–310. – reference: T. Warren Liao, Clustering of time series data: A survey, Pattern Recognition, 38 (11) (2005) pp. 1857–1874. – reference: W. H. Kruskal and W. A. Wallis, Use of ranks in one-criterion variance analysis, Journal of the American Statistical Association, 47 (260) (1952) pp. 583–621. – reference: M. A. Smith, B. Freidlin, L. A. G. Ries, and R. Simon, Trends in Reported Incidence of Primary Malignant Brain Tumors in Children in the United States, Journal of the National Cancer Institute, 90 (17) (1998) pp. 1269–1277. – reference: P. C. Mahalanobis, On the generalized distance in statistics, Proceedings of the National Institute of Sciences (Calcutta), 2 (1936) pp. 49–55. – reference: R. De Maesschalck, D. Jouan-Rimbaud, and D. L. Massart, The Mahalanobis Distance, Chemometrics and Intelligent Laboratory Systems, 50 (1) (2000) pp. 1–18. – reference: G. J. Szekely, Hierarchical clustering via joint between-within distances: Extending ward’s minimum variance method, Journal of Classification, 22 (2005) pp. 151–183. – reference: Y. Xiong, Mixtures of ARMA models for model-based time series clustering, Data Mining, ICDM proceedings, (2002) pp. 717–720. – reference: E. J. Keogh and M. J. Pazzani, An enhanced Representation of Time Series which Allows Fast and Accurate Classification, Clustering and Relevance Feedback, KDD-98 Proceedings, (1998) pp. 239–278. – reference: S. Deorah, C. F. Lynch, Z. A. Sibenaller, and T. C. Ryken, Trends in brain cancer incidence and survival in the United States: Surveillance, Epidemiology, and End Results Program, 1973 to 2001, Neurosurgical Focus, 20 (4) (2006) pp. E1. – reference: K. Kalpakis, D. Gada, and V. Puttagunta, Distance Measures for Effective Clustering of ARIMA Time Series, Data Mining, (ICDM2001) (2001) pp. 273–280. – reference: X. Wang and R. Hyndman, Characteristic-Based Clustering for Time Series Data, Data Mining and Knowledge Discovery, 13 (2006) pp. 335–364. – reference: C. Hervada-Sala and E. Jarauta-Bragulat, A Program to Perform Ward’s Clustering Method on Several Regionalized Variables, Computers & Geosciences, 30 (8) (2004) pp. 881–886. – reference: E.Theodorsson-Norheim, Kruskal-Wallis Test: BASIC Computer Program to Perform Nonparametric One-way Analysis of Variance and Multiple Comparisons on Ranks of Several Independent Samples, Computer Methods and Programs in Biomedicine, 23 (1) (1986) pp. 57–62. – reference: T. A. Dolecek, J. M. Propp, N. E. Stroup, and C. Kruchko, CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2005?2009, Neuro-Oncology, 14 (5) (2012) pp. v1–v49. – reference: M. Corduas and D. Piccolo, Time Series Clustering and Classification by the Autoregressive Metric, Computational Statistics & Data Analysis, 52 (4) (2008) pp. 1860–1872. – reference: Y. Xiong and D. Yeung, Time Series Clustering with ARMA Mixtures, Pattern Recognition, 37 (8) (2004) pp. 1675–1689. |
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| Title | Active and Dynamic Approaches for Clustering Time Dependent Information: Lag Target Time Series Clustering and Multi-Factor Time Series Clustering |
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