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ZENODO
Software . 2019
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Software . 2019
Data sources: ZENODO
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Opt_NC_ERP

Authors: Reza Mahini;

Opt_NC_ERP

Abstract

The current demo toolbox predicts an optimal number of clusters for two components of interest in simulated ERP data “Data_LN_N.mat” using Consensus Clustering method. The toolbox is open source, you are welcome to leave your opinion about it. Respect to Academic rules please cite the toolbox if you find it useful and use it in your work! Here is how to cite: Reza Mahini. (2019, July 22). Opt_NC_ERP (Version v1.0.0). Zenodo. http://doi.org/10.5281/zenodo.3345259 For easier using use may follow the below steps: 1- Unzip the compressed file in a folder. 2- Set the mentioned folder as default for MATLAB (version 2015 and upper) 3- Run the file named "Opt_NC_ERP_Sim.m" which is the main script of the toolbox 4- For easier access the consensus clustering results from various clustering methods, namely, K-means, Hierarchical clustering, FCM, SOM, Diffusion map spectral clustering for ensembling cluster, we have provided the results from them in 100 iterative runnings in the file named "compGroup_CC_LN_N.mat" which will be loaded on MATLAB for further steps. 5- By running the code, you will able to see the plotted results and various data tables about the processing results which we did not plot all of them in this version. You are welcome to use the toolbox, we will be glad to hear your opinion about the toolbox. Cheers, Reza Mahini

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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