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Approximation techniques for clustering dissimilarity data
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DOI：10.1016/j.neucom.2012.01.033.png)
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Abstract
En 中文
Recently, diverse high quality prototype-based clustering techniques have been developed which can directly deal with data sets given by general pairwise dissimilarities rather than standard Euclidean vectors. Examples include affinity propagation, relational neural gas, or relational generative topographic mapping. Corresponding to the size of the dissimilarity matrix, these techniques scale quadratically with the size of the training set, such that training becomes prohibitive for large data volumes. In this contribution, we investigate two different linear time approximation techniques, patch processing and the Nystrom approximation. We apply these approximations to several representative clustering techniques for dissimilarities, where possible, and compare the results for diverse data sets. (C) 2012 Elsevier B.V. All rights reserved.
Keywords:
Clustering dissimilarity data
Approximation
Patch processing
Nystrom approximation
Affinity propagation
Generative topographic mapping
Neural gas
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