Cluster ensembles --- a knowledge reuse framework for combining multiple partitions

By Alexander Strehl, Joydeep Ghosh. 2003

345 citations recorded by the source corpus.

This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings. We first identify several application scenarios for the resultant 'knowledge reuse' framework that we call cluster ensembles. The cluster ensemble problem is then formalized as a combinatorial optimization problem in terms of shared mutual information. In addition to a direct maximization approach, we propose three effective and efficient techniques for obtaining high-quality combiners (consensus functions). The first combiner induces a similarity measure from the partitionings and then reclusters the objects. The second combiner is based on hypergraph partitioning. The third one collapses groups of clusters into meta-clusters which then compete for each object to determine the combined clustering. Due to the low computational costs of our techniques, it is quite feasible to use a supra-consensus function that evaluates all three approaches against the objective function and picks the best solution for a given situation. We evaluate the effectiveness of cluster ensembles in three qualitatively different application scenarios: (i) where the original clusters were formed based on non-identical sets of features, (ii) where the original clustering algorithms worked on non-identical sets of objects, and (iii) where a common data-set is used and the main purpose of combining multiple clusterings is to improve the quality and robustness of the solution. Promising results are obtained in all three situations for synthetic as well as real data-sets.

Subfields: General Health Professions, Philosophy

View Paper: https://www.semanticscholar.org/paper/992f1fc92017bc1849f8dc7472a545bbc99e9803

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Citations in the corpus

Knowledge transfer via multiple model local structure mapping (2008, methodology): “[26] A.”
Clustering single-cell RNA-seq data with a model-based deep learning approach (2019, methodology): “It retains perfect clustering concordance (NMI ≈ 1) until r min decreases to 0.3, and its performance remains decent (NMI > 0.85) even when the imbalance is extreme ( r min = 0.1).”
SC3 - consensus clustering of single-cell RNA-Seq data (2016, neutral): “5 Consensus clustering— SC3 computes a consensus matrix using the Cluster-based Similarity Partitioning Algorithm (CSPA)22.”
A Brain-Wide Study of Age-Related Changes in Functional Connectivity. (2015, background): “NMI measures how much information is provided by one set of assignments about another set of assignments (Strehl and Ghosh 2003) and varies from 0 (no mutual information) to 1 (identical node assignments).”
Using diversity in cluster ensembles (2004, methodology): “Cluster ensemble methods with direct optimization have been proposed in [15, 17].”