# Consensus clustering

Consensus clustering is an important elaboration of traditional cluster analysis.wikipedia

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### Ensemble learning

**ensembles of classifiersensembleBayesian model averaging**

Consensus clustering for unsupervised learning is analogous to ensemble learning in supervised learning.

By analogy, ensemble techniques have been used also in unsupervised learning scenarios, for example in consensus clustering or in anomaly detection.

### Alexander Strehl

His areas of expertise are machine learning, consensus clustering, business intelligence, big data, artificial intelligence, cluster analysis, data mining, entrepreneurship and digital transformation.

### Cluster analysis

**clusteringdata clusteringcluster**

Consensus clustering is an important elaboration of traditional cluster analysis.

### NP-completeness

**NP-completeNP completeNP-complete problem**

When cast as an optimization problem, consensus clustering is known as median partition, and has been shown to be NP-complete, even when the number of input clusterings is three.

### Self-organizing map

**Kohonen networkSOMHebbian model**

Iterative descent clustering methods, such as the SOM and k-means clustering circumvent some of the shortcomings of hierarchical clustering by providing for univocally defined clusters and cluster boundaries.

### K-means clustering

**k-meansk''-means clusteringk-means algorithm**

Iterative descent clustering methods, such as the SOM and k-means clustering circumvent some of the shortcomings of hierarchical clustering by providing for univocally defined clusters and cluster boundaries.

### Hierarchical clustering

**agglomerative hierarchical clusteringhierarchical cluster analysisdivisive clustering**

Iterative descent clustering methods, such as the SOM and k-means clustering circumvent some of the shortcomings of hierarchical clustering by providing for univocally defined clusters and cluster boundaries.

### Feature (machine learning)

**feature vectorfeature spacefeatures**

To reduce the false positive potential in clustering samples (observations), Şenbabaoğlu et al recommends (1) doing a formal test of cluster strength using simulated unimodal data with the same feature space correlation structure as in the empirical data, (2) not relying solely on the consensus matrix heatmap to declare the existence of clusters, or to estimate optimal K, (3) applying the proportion of ambiguous clustering (PAC) as a simple yet powerful method to infer optimal K.

### Genetic algorithm

**genetic algorithmsgeneticDarwinian algorithm**

They proposed information theoretic distance measures, and they propose genetic algorithms for finding the best aggregation solution.

### Kullback–Leibler divergence

**relative entropyKullback-Leibler divergenceinformation gain**

We can define a distance measure between two instances using the Kullback–Leibler (KL) divergence, which calculates the “distance” between two probability distributions.

### Bayesian probability

**Bayesiansubjective probabilityBayesianism**

BCC defines a fully Bayesian model for soft consensus clustering in which multiple source clusterings, defined by different input data or different probability models, are assumed to adhere loosely to a consensus clustering.

### List of statistics articles

**List of statistical topicsList of statistics topicsIndex of statistics articles**

### Outline of machine learning

**machine learning algorithmslearning algorithmsmachine learning**

### Bioinformatics

**bioinformaticbioinformaticianbio-informatics**

Examples of clustering algorithms applied in gene clustering are k-means clustering, self-organizing maps (SOMs), hierarchical clustering, and consensus clustering methods.

### Immunological constant of rejection

The consensus clustering of tumours based on ICR gene expression provides an assessment of the prognosis and response to immunotherapy.