ccHBGF - Overview

ccHBGF - Overview#

Overview of Consensus Clustering Workflow

A python consensus clustering function utilising Hybrid Bipartite Graph Formulation (HBGF). HBGF is a graph-based consensus multi-source clustering technique. This method constructs a bipartite graph with two types of vertices: observations and clusters from different clusteirng solutions. An edge exists only between an observation vertex and a cluster vertex, indicating the object’s membership in that cluster. The graph is then partitioned using spectral partitioning to derive consensus labels for all observations.

Overview#

Overview of Consensus Clustering Workflow

  1. Definition of a bipartite graph adjacency matrix A

  2. Decomposition of A into a spectral embedding UVt

  3. Clustering of UVt into consensus labels

Installation#

pip install ccHBGF

Example Usage#

from ccHBGF import ccHBGF

consensus_labels = ccHBGF(solutions_matrix, init='orthogonal', tol=0.1, verbose=True, random_state=0)

Where the solutions_matrix is of shape (m, n):

  • m = the number of observations

  • n = the number of different clustering solutions.

References#

  1. Hu, Tianming, et al. “A comparison of three graph partitioning based methods for consensus clustering.” Rough Sets and Knowledge Technology: First International Conference, RSKT 2006, Chongquing, China, July 24-26, 2006. Proceedings 1. Springer Berlin Heidelberg, 2006.

  2. Fern, Xiaoli Zhang, and Carla E. Brodley. “Solving cluster ensemble problems by bipartite graph partitioning.” Proceedings of the twenty-first international conference on Machine learning. 2004.

  3. Ng, Andrew, Michael Jordan, and Yair Weiss. “On spectral clustering: Analysis and an algorithm.” Advances in neural information processing systems 14 (2001).