Data clustering algorithm based on agglomerative hierarchical clustering (AHC) which uses minimum volume increase (MVI) and minimum direction change (MDC) clustering criteria.
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Updated
Jan 12, 2016 - MATLAB
Data clustering algorithm based on agglomerative hierarchical clustering (AHC) which uses minimum volume increase (MVI) and minimum direction change (MDC) clustering criteria.
A prior learning and sampling model informed tool for learning with Single Cell RNA-Seq data
A simple program which performs K-Means clustering on a data set as well as visualizes the results.
Clustering analysis using an evolutionary optimization algorithm based on nature, Forest Optimization Algorithm
Partition relevance analysis with the reduction step
Localization Analyzer for Nanoscale Distributions (LAND) - 2D and 3D Analysis of SMLM Data
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This is the first release of the repository containing the MATLAB functions relative to the paper 'Business models of the Banks in the Euro Area', No. 2070, ECB Working Paper (https://www.ecb.europa.eu/pub/pdf/scpwps/ecb.wp2070.en.pdf?ee58f8028aa3d7b55dd977292218b268), by Matteo Farnè and Angelos Vouldis.
Pepelka is a MATLAB toolbox for data clustering and visualization.
Density-Based Clustering Validation
CVIK is a Toolbox for the automatic determination of the number of clusters on data clustering problems
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