کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
410683 679157 2012 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Approximation techniques for clustering dissimilarity data
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Approximation techniques for clustering dissimilarity data
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 90, 1 August 2012, Pages 72–84
نویسندگان
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