Robust Ensemble Clustering Using Probability Trajectories

Overview

This repository provides the MATLAB code for two ensemble clustering algorithms, namely, probability trajectory accumulation (PTA) and probability trajectory based graph partitioning (PTGP). If you find the code useful for your research,please cite the paper below.

Dong Huang, Jian-Huang Lai, and Chang-Dong Wang. 
Robust Ensemble Clustering Using Probability Trajectories, 
IEEE Transactions on Knowledge and Data Engineering, 2016, 28(5), pp.1312-1326.

Description of Files

There are mainly two types of materials in this directory:

1. A pool of 200 base clusterings for each dataset;
2. The code of the PTA and PTGP algorithms.

Data

The base clustering pools for the ten datasets are provided in the following MAT files:

bc_pool_MF.mat
bc_pool_IS.mat
bc_pool_MNIST.mat
bc_pool_ODR.mat
bc_pool_LS.mat
bc_pool_PD.mat
bc_pool_USPS.mat
bc_pool_FC.mat
bc_pool_KDD99_10P.mat
bc_pool_KDD99.mat

There are two variables in the MAT file for each dataset, namely, members and gt. The variable gt is the ground-truth label, which is an N-dimension vector. The variable members is an N x s matrix, where each column of it is a candidate base clustering.

Code

The file entitled 'demo_PTA_and_PTGP.m' is the main file for running PTA and PTGP. You may change the following settings in order to test the performance of PTA and PTGP:

1) dataName:	the dataset to be used.
2) M:		the ensemble size.
3) cntTimes:	run PTA and PTGP for cntTimes times and obtain the average performance.
4) para.K:	the parameter K.
5) para.T:	the parameter T.
6) clsNums:	a vector of positve integers, specifying different numbers of clusters for PTA and PTGP.

The execution results and the variable 'bcIdx' will be saved in results.mat. The bcIdx is a cntTimes x M matrix and stores the information of the ensembles. Each row in bcIdx includes M indices for choosing base clusterings in the pool and thus represents an ensemble of M base clusterings. When comparing our approach to other approaches, please make sure that they use the same base clustering settings, i.e., use the ensembles generated by the same 'bcIdx'.

Questions?

Don't hesitate to contact me if you have any questions regarding this work.
Email: huangdonghere at gmail dot com
Website: https://www.researchgate.net/publication/284259332