% This is README file for the experiments of Convex Hull Data Description %% 1. The description of the folders and files % Files in folder "datasets" are data files used for the experiments; % Files in folder "methods" contains methods to be compared with CHDD; % Files in folder "tools" contains the framework that CHDD built upon; % Files in folder "results" maintains the results of the experiments; % Files in folder "figures" has all the source code for reproducing the % figures in the paper. % "DD_Method_*.m" are step-by-step introductions of CHDD; % "chdd.m" is the file that integrates CHDD into dd_tools framework; % "chdc_train.m" and "chdc_test.m" are for convex hull clustering; % "Func_KKmeans.m" is a simple wrapper for lmkkmeans; (Please check ref) % "Func_AMI.m" is for calculating AMI of clustering results; (Please check ref) % "Comparison_*.m" are files for the comparison of CHDD with other method % in one-class classification or clustering tasks. %% 2. A simple case study to show how to use the programs % use separate programs to show the whole process of using CHDD % Run: DD_Method_XW_1Tra % training % Run: DD_Method_XW_2Thr % thresholding % Run: DD_Method_XW_3Tst % testing %% 3. Test the validity of chdd function under the dd_tools framework % please run all the files in step 2 at first t = T(1:2,:)'; Q = chdd(Ring2',0.15,0.5,1.0e-4); res = +(t*Q); figure(3) hold on scatter(Q.data.sv(:,1), Q.data.sv(:,2), 'bo') scatter(T(1,:),T(2,:),'kx') scatter(T(1,abs(res(:,1))>Q.data.threshold),T(2,abs(res(:,1))>Q.data.threshold),'go') hold off %% 4. References, please refer to the following for more details % dd_tools: http://prlab.tudelft.nl/david-tax/dd_tools.html % prtools: http://prtools.org/software/ % dbscan: https://uk.mathworks.com/matlabcentral/fileexchange/52905-dbscan-clustering-algorithm % kkmeans: https://github.com/mehmetgonen/lmkkmeans