VisClust

This is visClust, a novel clustering algorithm based on theory from mathematical imaging and linear projections.

This repository contains the implementation of visClust: A visual clustering algorithm based on orthogonal projections, Anna Breger, Clemens Karner, Martin Ehler, 2023.

Written by Anna Breger and Clemens Karner
University of Vienna, Faculty of Mathematics
Vienna, Austria
https://homepage.univie.ac.at/anna.breger/ https://homepage.univie.ac.at/clemens.karner/

This software is licensed under the MIT license, see the file 'LICENSE'.

For all questions, bugs and suggestions please email clemens.karner@univie.ac.at or anna.breger@univie.ac.at

Usage instructions

The file 'example.m' contains all possible parameter configurations applied to the Iris dataset.

Example:

X = iris_dataset'
[prediction, projector] = visclust(X,3)

VisClust Unsupervised classification via visual clustering

• IDX = VISCLUST(X, OPTIONAL) returns a suggested number of clusters for N data points of dimension D, stored in an N-by-D matrix X.

• IDX = visclust(X, NUMCLUSTERS, OPTIONAL) partitions N data points of dimension D, stored in an N-by-D matrix X, into K clusters. VISCLUST returns a vector IDX of length N containing the cluster indices of each data point.

• [IDX, P] = visclust(X, NUMCLUSTERS, OPTIONAL) returns a cell P containing the optimal projection(s) identified by visClust.

• [ ... ] = visclust(..., 'PARAM1',val1, 'PARAM2',val2, ...) specifies optional parameter name/value pairs to control visClust. Parameters are:

  • 'division'... Wanted division of clusters. Choices are:

    • '0'... Uniform division (default).
    • K-by-1 matrix... Wanted relative division, e.g. division = [0.4,0.4,0.2].

  • 'thresh... Allowed deviation of wanted division, ideally between 0.01 and 0.3. Default: 0.1 (corresponds to 10%)

  • 'scaling'... Parameter to control the standard deviation in the Gaussian filters, ideally between 0.5 and 2. Default: 1.25

  • 'subsampling'... Number of data points clustered without subsampling. Default: 10000

  • 'projections'... Projections to be used for clustering. Choices are:

    • 'random'... Random orthogonal projections (default).
    • 'pca'... Projector obtained by Principal Component Analysis.
    • 'tsne'... Dimension reduction using the t-Distributed Stochastic Neighbor Embedding method.
    • M-by-2 cell... M projectors stored in a M-by-2 cell. The first cell dimension stores the matrices of projections to R^2 (shape Dx2), if available the projections to R^3 are stored in the second dimension (shape Dx3).

  • 'method'... Clustering method. Choices are:

    • 'all'... Runs through all methods (default).
    • 'all2'... 2D clustering.
    • 'all3'... 3D clustering.
    • 'vis2'... Simultaneous clustering in 2D.
    • 'vis3'... Simultaneous clustering in 3D.
    • 'pvis2'... Partial clustering in 2D.
    • 'pvis3'... Partial clustering in 3D.

  • 'showimg'... Generates a plot of projected clustering. Choices are:

    • 'false'... No plot (default).
    • 'true'... Generates a plot.
    • 'PATH'... Path to store the generated plot.