Unsupervised mining of long time series based on latent topic model
This paper presents a novel unsupervised method for mining time series based on two generative topic models, i.e., probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA). The proposed method treats each time series as a text document, and extracts a set of local patterns...
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| Published in: | Neurocomputing (Amsterdam) Vol. 103; pp. 93 - 103 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Amsterdam
Elsevier B.V
01.03.2013
Elsevier |
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | This paper presents a novel unsupervised method for mining time series based on two generative topic models, i.e., probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA). The proposed method treats each time series as a text document, and extracts a set of local patterns from the sequence as words by sliding a short temporal window along the sequence. Motivated by the success of latent topic models in text document analysis, latent topic models are extended to find the underlying structure of time series in an unsupervised manner. The clusters or categories of unlabeled time series are automatically discovered by the latent topic models using bag-of-patterns representation. The proposed method was experimentally validated using two sets of time series data extracted from a public Electrocardiography (ECG) database through comparison with the baseline k-means and the Normalized Cuts approaches. In addition, the impact of the bag-of-patterns' parameters was investigated. Experimental results demonstrate that the proposed unsupervised method not only outperforms the baseline k-means and the Normalized Cuts in learning semantic categories of the unlabeled time series, but also is relatively stable with respect to the bag-of-patterns' parameters. To the best of our knowledge, this work is the first attempt to explore latent topic models for unsupervised mining of time series data.
► A novel unsupervised method based on latent topic modelis proposedfor mining time series. ► The proposed method processes time series as text documents. ► The topic model based method is able to effectively capture structural similarity information. ► The proposed method can be potentially used for time series segmentation. |
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| AbstractList | This paper presents a novel unsupervised method for mining time series based on two generative topic models, i.e., probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA). The proposed method treats each time series as a text document, and extracts a set of local patterns from the sequence as words by sliding a short temporal window along the sequence. Motivated by the success of latent topic models in text document analysis, latent topic models are extended to find the underlying structure of time series in an unsupervised manner. The clusters or categories of unlabeled time series are automatically discovered by the latent topic models using bag-of-patterns representation. The proposed method was experimentally validated using two sets of time series data extracted from a public Electrocardiography (ECG) database through comparison with the baseline k-means and the Normalized Cuts approaches. In addition, the impact of the bag-of-patterns' parameters was investigated. Experimental results demonstrate that the proposed unsupervised method not only outperforms the baseline k-means and the Normalized Cuts in learning semantic categories of the unlabeled time series, but also is relatively stable with respect to the bag-of-patterns' parameters. To the best of our knowledge, this work is the first attempt to explore latent topic models for unsupervised mining of time series data. This paper presents a novel unsupervised method for mining time series based on two generative topic models, i.e., probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA). The proposed method treats each time series as a text document, and extracts a set of local patterns from the sequence as words by sliding a short temporal window along the sequence. Motivated by the success of latent topic models in text document analysis, latent topic models are extended to find the underlying structure of time series in an unsupervised manner. The clusters or categories of unlabeled time series are automatically discovered by the latent topic models using bag-of-patterns representation. The proposed method was experimentally validated using two sets of time series data extracted from a public Electrocardiography (ECG) database through comparison with the baseline k-means and the Normalized Cuts approaches. In addition, the impact of the bag-of-patterns' parameters was investigated. Experimental results demonstrate that the proposed unsupervised method not only outperforms the baseline k-means and the Normalized Cuts in learning semantic categories of the unlabeled time series, but also is relatively stable with respect to the bag-of-patterns' parameters. To the best of our knowledge, this work is the first attempt to explore latent topic models for unsupervised mining of time series data. ► A novel unsupervised method based on latent topic modelis proposedfor mining time series. ► The proposed method processes time series as text documents. ► The topic model based method is able to effectively capture structural similarity information. ► The proposed method can be potentially used for time series segmentation. |
| Author | Wang, Jin Kouzani, Abbas Nahavandi, Saeid Sun, Xiangping She, Mary F.H. |
| Author_xml | – sequence: 1 givenname: Jin surname: Wang fullname: Wang, Jin email: jay.wangjin@gmail.com organization: Institute for Technology Research and Innovation, Deakin University, Geelong, VIC 3217, Australia – sequence: 2 givenname: Xiangping surname: Sun fullname: Sun, Xiangping organization: Institute for Technology Research and Innovation, Deakin University, Geelong, VIC 3217, Australia – sequence: 3 givenname: Mary F.H. surname: She fullname: She, Mary F.H. organization: Institute for Technology Research and Innovation, Deakin University, Geelong, VIC 3217, Australia – sequence: 4 givenname: Abbas surname: Kouzani fullname: Kouzani, Abbas organization: School of Engineering, Deakin University, Geelong, VIC 3217, Australia – sequence: 5 givenname: Saeid surname: Nahavandi fullname: Nahavandi, Saeid organization: Center for Intelligent Systems Research, Deakin University, Geelong, VIC 3217, Australia |
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| Keywords | LDA ECG signals Bag-of-patterns Unsupervised learning pLSA Text analysis K means algorithm Algebraic semantic Data mining Modeling Document processing Business process Graph decomposition Bag of words Classification Sliding window Database Electrocardiography Linguistics Natural language Categorization Document analysis Generative model Concept learning Probabilistic approach Time series Text Experimental result Document structure |
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| SubjectTerms | Applied sciences Artificial intelligence Bag-of-patterns Biological and medical sciences Categories Computer science; control theory; systems Data processing. List processing. Character string processing Dirichlet problem ECG signals Electrocardiography. Vectocardiography Electrodiagnosis. Electric activity recording Exact sciences and technology Inference from stochastic processes; time series analysis Investigative techniques, diagnostic techniques (general aspects) LDA Mathematical models Mathematics Medical sciences Memory organisation. Data processing Mining pLSA Probability and statistics Sciences and techniques of general use Semantics Software Speech and sound recognition and synthesis. Linguistics Statistics Texts Time series Unsupervised learning |
| Title | Unsupervised mining of long time series based on latent topic model |
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