Consensus clustering

An implementation of Consensus clustering in Python

This repository contains a Python implementation of consensus clustering, following the paper Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data.

ConsensusCluster

The class containing the implementation.

Attributes

• cluster : the class to perform the clustering (like KMEANS from sklearn)
  • NOTE: the class is to be instantiated with parameter n_clusters, and possess a fit_predict method, which is invoked on data.
• L : smallest number of clusters to try
• K : largest number of clusters to try
• H : number of resamplings for each number of clusters
• resample_proportion : percentage to sample
• Mk : consensus matrices for each k (shape =(K,data.shape[0],data.shape[0]))
  • NOTE: every consensus matrix is retained, like specified in the paper
• Ak : area under CDF for each number of clusters
  • (see paper: section 3.3.1. Consensus distribution.)
• deltaK : changes in areas under CDF
  • (see paper: section 3.3.1. Consensus distribution.)
• bestK : number of clusters that was found to be best

Methods

ConsensusCluster.__init__

Parameters:
    * cluster : the class to perform the clustering (like KMEANS from sklearn)
      * NOTE: the class is to be instantiated with parameter `n_clusters`,
        and possess a `fit_predict` method, which is invoked on data.
    * L : smallest number of clusters to try
    * K : largest number of clusters to try
    * H : number of resamplings for each number of clusters
    * resample_proportion : percentage to sample

ConsensusCluster.fit

Fits all attributes of the class to data

Parameters:
    * data : data.shape == (n_examples,n_features) 
    * verbose : should print or not

ConsensusCluster.predict

Predicts the clustering on the consensus matrix, for best found number of cluster

Returns:
    * Cluster labels for each example

ConsensusCluster.predict_data

Predicts the clustering on the data, for best found number of cluster

Parameters:
    * data : data.shape == (n_examples,n_features)

Returns:
    * Cluster labels for each example