Graphical Toolbox for Clustering and Classification of Data Frames
Abundances barplot
Abundances barplots inside Tk tabs.
Abundances barplots inside Tk tabs.
Clustering addition
Adding Ids To a Sampling
Add operation
Plot for data exploration/analysis
Preprocessing application
Spectral clustering
Batch process tab
Clustering loading
Constraints matrices
Build Import tab
Build Name Operation
build Preprocess tab
Semi-Supervised tab
Supervised tab
Unsupervised tab
Clusters density computation
Clusters summaries computation
Constrained K-means clustering
Constrained Spectral Clustering
Expectation-Maximization clustering
Gap computation
Gap computation
Gaussian similarity
Gaussian similarity
Prediction of number of cells in colonies
GUI to estimate the number of cells in colonies for each cluster
K-means clustering
Number of dimensions for PCA
Principal Components Analysis
Sampling raw data matrix
Semi-supervised clustering
Spectral embedding
Supervised classification
Unsupervised clustering
Conversion of a set of names pairs to matrix of index pairs (2 columns...
Conversion of element names to indexes
Correlation test.
Manually counting the number of cells in colonies
GUI to manually count the number of cells in colonies
Results directories creation
Multiple Normalized Cut
detail Operation
Logo frame in the graphical user interface
Parameters dropping
Elbow Finder
Elbow Plot.
Extraction of features from a summary object.
Prototypes extraction
Feature Space Name Conversion
Automatic estimation of the number of clusters
Labels formatting
Format Parameter List
File Encoding Identification.
Images clustering
Labels importation
Sample importation
batch tab
import tab
Parameters initialization
build Preprocess tab
Semi-Supervised tab
supervised tab
Unsupervised tab
Predictive models computation for the number of cells in colonies
Kmeans clustering with automatic estimation of number of clusters
Quick kmeans clustering
Semi-supervised spectral clustering
Builds list of derivable feature spaces
Preprocessing loading
Previous clustering results loading
Sample loading
Summaries loading
Main window
Make operation config object to build feature spaces
RclusTool makeTitle.
Match Names
Rates of constraints satisfaction
Multiple Normalized Cut
RclusTool consoleMessage.
Clusters renaming
plot Variables Density
Profile and image plotting
Profile and image plotting
2D-features scatter-plot
Preview CSV file
Sample purging
Username and user type selection
Training set reading
Zeros replacement
Object saving
Clustering saving
Count saving
Log file saving
Manual prototypes saving
Preprocessing exportation
Clusters summaries saving
Search neighbour
Signals clustering
Character vector numeric sorting
Clusters labels sorting
Spectral clustering
Spectral clustering
Spectral embedding
Add notetab.
Delete notetab inside a tk-notebook
Draw in a Notetab.
RclusTool tk2notetab.
RclusTool tkEmptyLine.
RclusTool tkrplot.
RclusTool tkrreplot.
To String Data Frame
Clusters names updating
Interactive figure with 2D scatter-plot
Graphical toolbox for clustering and classification of data frames. It proposes a graphical interface to process clustering and classification methods on features data-frames, and to view initial data as well as resulted cluster or classes. According to the level of available labels, different approaches are proposed: unsupervised clustering, semi-supervised clustering and supervised classification. To assess the processed clusters or classes, the toolbox can import and show some supplementary data formats: either profile/time series, or images. These added information can help the expert to label clusters (clustering), or to constrain data frame rows (semi-supervised clustering), using Constrained spectral embedding algorithm by Wacquet et al. (2013) <doi:10.1016/j.patrec.2013.02.003> and the methodology provided by Wacquet et al. (2013) <doi:10.1007/978-3-642-35638-4_21>.