ccHBGF 0.1.0

ccHBGF - consensus clustering using Hybrid Bipartite Graph Formulation (HBGF)

ccHBGF: Graph-based Consensus Clustering

A python-based consensus clustering function utilising Hybrid Bipartite Graph Formulation (HBGF).

The ccHBGF function performs consensus clustering by following these steps:

1. Definition of a bipartite graph adjaceny matrix A
2. Decomposition of A into a spectral embedding UVt
3. Clustering of UVt into a consensus labels

Installation

pip install ccHBGF
pip install 'ccHBGF[tutorial]' # When running example notebooks

Hybrid Bipartite Graph Formulation (HBGF)

HBGF is a graph-based consensus ensemble 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.

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.

Please refer to notebooks/ for more detailed examples.

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).