ClusterEnsembles

A Python package for cluster ensembles. Cluster ensembles generate a single consensus clustering label by using base labels obtained from multiple clustering algorithms. The consensus clustering label stably achieves a high clustering performance.

Installation

pip install ClusterEnsembles

Usage

CE.cluster_ensembles is used as follows.

>>> import numpy as np

>>> import ClusterEnsembles as CE

>>> label1 = np.array([1, 1, 1, 2, 2, 3, 3])

>>> label2 = np.array([2, 2, 2, 3, 3, 1, 1])

>>> label3 = np.array([4, 4, 2, 2, 3, 3, 3])

>>> label4 = np.array([1, 2, np.nan, 1, 2, np.nan, np.nan]) # `np.nan`: missing value

>>> labels = np.array([label1, label2, label3, label4])

>>> label_ce = CE.cluster_ensembles(labels)

>>> print(label_ce)
[1 1 1 2 2 0 0]

Parameters

labels: numpy.ndarray

Labels generated by multiple clustering algorithms such as K-Means.

Note: Assume that the length of each label is the same.
nclass: int, default=None

Number of classes in a consensus clustering label. If nclass=None, set the maximum number of classes in each label except missing values. In other words, set nclass=3 automatically in the above.
solver: {'cspa', 'hgpa', 'mcla', 'hbgf', 'nmf', 'all'}, default='hbgf'

'cspa': Cluster-based Similarity Partitioning Algorithm [1].

'hgpa': HyperGraph Partitioning Algorithm [1].

'mcla': Meta-CLustering Algorithm [1].

'hbgf': Hybrid Bipartite Graph Formulation [2].

'nmf': NMF-based consensus clustering [3].

'all': The consensus clustering label with the largest objective function value [1] is returned among the results of all solvers.

Note: Please use 'hbgf' for large-scale labels.
random_state: int, default=None

Used for 'hgpa', 'mcla', and 'nmf'. Please pass an integer for reproducible results.
verbose: bool, default=False

Whether to be verbose.

Return

label_ce: numpy.ndarray

A consensus clustering label generated by cluster ensembles.

Similar Package

GGiecold/Cluster_Ensembles: https://github.com/GGiecold/Cluster_Ensembles

References

[1] A. Strehl and J. Ghosh, "Cluster ensembles -- a knowledge reuse framework for combining multiple partitions," Journal of Machine Learning Research, vol. 3, pp. 583-617, 2002.

[2] X. Z. Fern and C. E. Brodley, "Solving cluster ensemble problems by bipartite graph partitioning," In Proceedings of the Twenty-First International Conference on Machine Learning, p. 36, 2004.

[3] T. Li, C. Ding, and M. I. Jordan, "Solving consensus and semi-supervised clustering problems using nonnegative matrix factorization," In Proceedings of the Seventh IEEE International Conference on Data Mining, pp. 577-582, 2007.

[4] J. Ghosh and A. Acharya, "Cluster ensembles," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 1, no. 4, pp. 305-315, 2011.