Approximation techniques for clustering dissimilarity data

作者：Zhu Xibin; Gi**recht Andrej; Schleif Frank Michael; Hammer Barbara^*

来源：Neurocomputing, 2012, 90: 72-84.

DOI：10.1016/j.neucom.2012.01.033

• Clustering dissimilarity data
• Approximation
• Patch processing
• Nystrom approximation
• Affinity propagation
• Generative topographic mapping
• Neural gas

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.

• 出版日期2012-8-1