Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A new approach to analyzing gene expression time series data
Proceedings of the sixth annual international conference on Computational biology
Transformation-Invariant Clustering Using the EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Translation-invariant mixture models for curve clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Transformation-Invariant Component Analysis
International Journal of Computer Vision
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In this paper we present a family of models and learning algorithms that can simultaneously align and cluster sets of multidimensional curves measured on a discrete time grid. Our approach is based on a generative mixture model that allows both local nonlinear time warping and global linear shifts of the observed curves in both time and measurement spaces relative to the mean curves within the clusters. The resulting model can be viewed as a form of Bayesian network with a special temporal structure. The Expectation-Maximization (EM) algorithm is used to simultaneously recover both the curve models for each cluster, and the most likely alignments and cluster membership for each curve. We evaluate the methodology on two real-world data sets, and show that the Bayesian network models provide systematic improvements in predictive power over more conventional clustering approaches.