Determining the Number of Clusters via Iterative Consensus Clustering

We use a cluster ensemble to determine the number of clusters, k, in a group of data. A consensus similarity matrix is formed from the ensemble using multiple algorithms and several values for k. A random walk is induced on the graph defined by the consensus matrix and the eigenvalues of the associa...

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Bibliographic Details
Published inarXiv.org
Main Authors Race, Shaina, Meyer, Carl, Valakuzhy, Kevin
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 05.08.2014
Subjects
Algorithms
Clustering
Computer Science - Computer Vision and Pattern Recognition
Computer Science - Learning
Eigenvalues
Random walk
Similarity
Statistics - Machine Learning
Transition probabilities
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Summary:We use a cluster ensemble to determine the number of clusters, k, in a group of data. A consensus similarity matrix is formed from the ensemble using multiple algorithms and several values for k. A random walk is induced on the graph defined by the consensus matrix and the eigenvalues of the associated transition probability matrix are used to determine the number of clusters. For noisy or high-dimensional data, an iterative technique is presented to refine this consensus matrix in way that encourages a block-diagonal form. It is shown that the resulting consensus matrix is generally superior to existing similarity matrices for this type of spectral analysis.
ISSN:2331-8422
DOI:10.48550/arxiv.1408.0967