An implementation of Consensus clustering in Python

Monte Carlo Reference-based Consensus Clustering

Fast consensus clustering in networks

A General Framework for Consensus Partitioning

The R package "sarlacc" contains a pipeline to analyse nanopore sequencing data. It trims adapter sequences, retrieves optional UMI's, clusters reads and produces a consensus sequence for each cluster after multiple sequence alignment.

Reducing imbalanced dataset (Undersampling) by Consensus Clustering (Simple Majority Voting function) and validating the changes using different classifier model with bagging and boosting techniques.

TKDE 2020: Ultra-Scalable Spectral Clustering and Ensemble Clustering (U-SPEC & U-SENC) #large-scale spectral clustering# #large-scale ensemble clustering#

Consensus and WECR K-Means clustering.

MATLAB Code for Locally Weighted Ensemble Clustering (IEEE TCYB 2018)

Under-sampling based consensus clustering is applied on the three best clustering algorithms found after applying several Clustering Algorithms like K-means, K-modes, K-prototypes , K-means++ and fuzzy K-means on the majority class data of the IMBALANCED colon dataset to produce a BALANCED dataset.

In this task, we had to write a multi-threaded OpenMP program, that will solve the consensus problem. If all the slave (child) processes agree on a single decision, then the processes will terminate displaying consensus reached.

A Java software package that implements Projective Clustering Ensembles (PCE)

MATLAB code for Enhanced Ensemble Clustering via Fast Propagation of Cluster-wise Similarities (IEEE TSMC-S 2021)

Consensus K-Means clustering R package

Reducing imbalanced dataset (Undersampling) by Consensus Clustering (Simple Majority Voting function) and validating the changes using different classifier model with bagging and boosting techniques.

MATLAB Code for Robust Ensemble Clustering Using Probability Trajectories (IEEE TKDE 2016)

MultiCons (Multiple Consensuses) algorithm

Clustered Leader Election by Seniority

Implementation of: Clustering of the structures by using "snakes & dragons" approach, or correlation matrix as a signal - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0223267