Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Clustering of time series data-a survey
Pattern Recognition
k-mean alignment for curve clustering
Computational Statistics & Data Analysis
Why Does Unsupervised Pre-training Help Deep Learning?
The Journal of Machine Learning Research
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
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In this paper, we present a novel way of analyzing and summarizing a collection of curves, based on piecewise constant density estimation. The curves are partitioned into clusters, and the dimensions of the curves points are discretized into intervals. The cross-product of these univariate partitions forms a data grid of cells, which represents a nonparametric estimator of the joint density of the curves and point dimensions. The best model is selected using a Bayesian model selection approach and retrieved using combinatorial optimization algorithms. The proposed method requires no parameter setting and makes no assumption regarding the curves; beyond functional data, it can be applied to distributional data. The practical interest of the approach for functional data and distributional data exploratory analysis is presented on two real world datasets.