The Core Code of Bregmanian Consensus Clustering

Problem Statement

In this study, we relate each data partition$p^i$to a connectivity matrix.$M^i$ Thus, there is also a connectivity matrix $M$ corresponding to the final optimal consensus partition $P^*$. As the connectivity matrix can also be seen as a data similarity matrix, the final data clusters can be discovered by running a few similarity-based clustering algorithms on $M$. The ideas for solving this problem are shown in the following figure.

Algorithms

There are four Bregmannian consensus clustering algorithms, which include Euclidean Distance, Exponential Distance,Kullback-Leibler Distance.The organization structure of our algorithm is shown in the following and the details of our algorithms can be seen in the paper.

​ |---algorithm1(Unweighted Bregmannian Consensus Clustering,UBCC)

​ |---algorigthm2(Weight Bregmannian Consensus Clustering,WBCC)

​ | |---EuWBCC

​ | |---expWBCC

​ | |---KLWBCC

​ |---algorigthm3(Unweight Bregman Consensus Clustering With Constraints,UBCCC)

​ |---algorigthm4(Weight Bregmannian Consensus Clustering With Constraints,WBCCC)

​ | |---SSKLWBCC

​ | |---SSEuWBCC

​ | |---SSexpWBCC

Requirements

Before starting to implement the consensus clustering algorithm, you need to prepare the Python3 environment with some packages, such as, cvxopt, numpy, sklearn, pandas, random, scipy,matplotlib.

Running

It can be run like:

optimal_M = eWBCC(partitions,precision,tradeoff)