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

Version 1.0.0.0 (5.88 KB) by Joseph Santarcangelo
Dynamic Time-Alignment (DTA) K-Means Kernel Clustering For Time Sequence Clustering
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Updated 11 Jan 2016
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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 (2026). 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
Platform Compatibility
Windows macOS Linux
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Acknowledgements

Inspired by: Dynamic Time Warping (DTW)

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1.0.0.0

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