Parallel Computing TEDA Clustering Algorithm

The source code of the parallel computing TEDA clustering algorithm
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Mise à jour 11 nov. 2018

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The package contains:

1. ParallelTEDAClustering.m - The source code of the parallel computing TEDA clustering algorithm;

2. demo.m - The demo

Reference:
Gu X., Angelov P.P., Gutierrez G., Iglesias J.A., Sanchis A. (2017) Parallel Computing TEDA for High Frequency Streaming Data Clustering. In: Angelov P., Manolopoulos Y., Iliadis L., Roy A., Vellasco M. (eds) Advances in Big Data. INNS 2016. Advances in Intelligent Systems and Computing, vol 529. Springer, Cham

Please cite this algorithm using the above reference if this code helps.

For any queries about the codes, please contact Prof. Plamen P. Angelov (p.angelov@lancaster.ac.uk) and Dr. Xiaowei Gu (x.gu3@lancaster.ac.uk)

Programmed by Xiaowei Gu

Citation pour cette source

Gu X., Angelov P.P., Gutierrez G., Iglesias J.A., Sanchis A. (2017) Parallel Computing TEDA for High Frequency Streaming Data Clustering. In: Angelov P., Manolopoulos Y., Iliadis L., Roy A., Vellasco M. (eds) Advances in Big Data. INNS 2016. Advances in Intelligent Systems and Computing, vol 529. Springer, Cham

Compatibilité avec les versions de MATLAB
Créé avec R2018a
Compatible avec toutes les versions
Plateformes compatibles
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Version Publié le Notes de version
1.0.1

Updated the reference

1.0.0