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• W2000278525 abstract "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 Nyström approximation. We apply these approximations to several representative clustering techniques for dissimilarities, where possible, and compare the results for diverse data sets." @default.
• W2000278525 created "2016-06-24" @default.
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• W2000278525 date "2012-08-01" @default.
• W2000278525 modified "2023-09-23" @default.
• W2000278525 title "Approximation techniques for clustering dissimilarity data" @default.
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• W2000278525 doi "https://doi.org/10.1016/j.neucom.2012.01.033" @default.
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