jsantarc/Dynamic-Time-Alignment-K-Means-Kernel-Clustering-For-Time-Sequence-Clustering

Dynamic Time-Alignment (DTA) K-Means Kernel Clustering For Time Sequence Clustering
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Updated 11 Jan 2016

This is a matlab implementation of Dynamic Time-Alignment (DTA) K-Means Kernel Clustering For Time Sequence Clustering. The code is similar to what I used in my paper [1]. The code first calculates the DTA Kernel matrix, then performs clustering on time series of different lengths.
Read me @:https://github.com/jsantarc/Dynamic-Time-Alignment-K-Means-Kernel-Clustering-For-Time-Sequence-Clustering/issues/1

Cite As

Joseph Santarcangelo (2024). jsantarc/Dynamic-Time-Alignment-K-Means-Kernel-Clustering-For-Time-Sequence-Clustering (https://github.com/jsantarc/Dynamic-Time-Alignment-K-Means-Kernel-Clustering-For-Time-Sequence-Clustering), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2011b
Compatible with any release
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Acknowledgements

Inspired by: Dynamic Time Warping (DTW)

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Version Published Release Notes
1.0.0.0

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To view or report issues in this GitHub add-on, visit the GitHub Repository.