Mixtures of ARMA Models for Model-Based Time Series Clustering (2002)
| Venue: | In Proceedings of the IEEE International Conference on Data Mining |
| Citations: | 16 - 1 self |
BibTeX
@INPROCEEDINGS{Xiong02mixturesof,
author = {Yimin Xiong and Dit-Yan Yeung},
title = {Mixtures of ARMA Models for Model-Based Time Series Clustering},
booktitle = {In Proceedings of the IEEE International Conference on Data Mining},
year = {2002},
pages = {717--720}
}
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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 patterns that are represented as sequences or time series possibly of di#erent lengths. We propose a model-based approach to this problem using mixtures of autoregressive moving average (ARMA) models. We derive an expectation-maximization (EM) algorithm for learning the mixing coe#cients as well as the parameters of the component models. The algorithm can determine the number of clusters in the data automatically. Experiments were conducted on a number of simulated and real datasets. Results from the experiments show that our method compares favorably with another method recently proposed by others for similar time series clustering problems.
