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Abstract
Key Takeaways
Introduction
Methods Based on Ensemble Coassociation Matrix
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Cluster ensembles

Profile image of Ayan AcharyaAyan Acharya

2011, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

https://doi.org/10.1002/WIDM.32
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11 pages

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Abstract

Cluster ensembles combine multiple clusterings of a set of objects into a single consolidated clustering, often referred to as the consensus solution. Consensus clustering can be used to generate more robust and stable clustering results compared to a single clustering approach, perform distributed computing under privacy or sharing constraints, or reuse existing knowledge. This paper describes a variety of algorithms that have been proposed to address the cluster ensemble problem, organizing them in conceptual categories that bring out the common threads and lessons learnt while simultaneously highlighting unique features of individual approaches.

Key takeaways
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  1. Cluster ensembles improve clustering quality and robustness by consolidating multiple base clusterings into a consensus solution.
  2. Consensus clustering can handle varying numbers of clusters and symbolic label alignment challenges across different base solutions.
  3. Key algorithms include probabilistic models, coassociation-based approaches, and heuristic methods for cluster ensemble design.
  4. Metrics like Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), and Variation of Information (VI) assess clustering quality.
  5. Applications of cluster ensembles range from document clustering to gene expression analysis, enhancing diverse data analysis tasks.

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