کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
409665 679083 2013 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Unsupervised mining of long time series based on latent topic model
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Unsupervised mining of long time series based on latent topic model
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 103, 1 March 2013, Pages 93–103
نویسندگان
, , , , ,