Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Locally adaptive dimensionality reduction for indexing large time series databases
ACM Transactions on Database Systems (TODS)
Querying Time Series Data Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
The Journal of Machine Learning Research
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Automated extraction and parameterization of motions in large data sets
ACM SIGGRAPH 2004 Papers
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A latent variable model for chemogenomic profiling
Bioinformatics
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Atomic Wedgie: Efficient Query Filtering for Streaming Times Series
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Analysis of human electrocardiogram for biometric recognition
EURASIP Journal on Advances in Signal Processing
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
iSAX: indexing and mining terabyte sized time series
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical Debugging Using Latent Topic Models
ECML '07 Proceedings of the 18th European conference on Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Structural Similarity in Time Series Data Using Bag-of-Patterns Representation
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Human Action Recognition by Semilatent Topic Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Heartbeat time series classification with support vector machines
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Clustering of time series data-a survey
Pattern Recognition
A review on time series data mining
Engineering Applications of Artificial Intelligence
Expectation-propagation for the generative aspect model
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Rotation-invariant similarity in time series using bag-of-patterns representation
Journal of Intelligent Information Systems
Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model
Computer Methods and Programs in Biomedicine
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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.