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Time series clustering with arma mixtures abstract.
| Content Provider | CiteSeerX |
|---|---|
| Author | Xiong, Yimin Yeung, Dit-Yan |
| Abstract | Clustering problems are central to many knowledge discovery and data mining tasks. However, most existing clustering methods can only work with fixed-dimensional representations of data patterns. In this paper, we study the clustering of data pat-terns that are represented as sequences or time series possibly of different lengths. We propose a model-based approach to this problem using mixtures of autore-gressive moving average (ARMA) models. We derive an expectation-maximization (EM) algorithm for learning the mixing coefficients as well as the parameters of the component models. To address the model selection problem, we use the Bayesian information criterion (BIC) to determine the number of clusters in the data. Ex-periments are conducted on a number of simulated and real datasets. Results from the experiments show that our method compares favorably with other methods proposed previously by others for similar time series clustering tasks. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Similar Time Series Mixing Coefficient Data Pat-terns Bayesian Information Criterion Model Selection Problem Model-based Approach Autore-gressive Moving Average Different Length Arma Mixture Abstract Real Datasets Component Model Time Series Fixed-dimensional Representation Data Mining Task Data Pattern Time Series Clustering |
| Content Type | Text |
