Spectral clustering based on similarity and dissimilarity criterion
العنوان: | Spectral clustering based on similarity and dissimilarity criterion |
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المؤلفون: | Fanzhang Li, Bangjun Wang, Caili Wu, Zhao Zhang, Li Zhang |
المصدر: | Pattern Analysis and Applications. 20:495-506 |
بيانات النشر: | Springer Science and Business Media LLC, 2015. |
سنة النشر: | 2015 |
مصطلحات موضوعية: | Fuzzy clustering, business.industry, Correlation clustering, Single-linkage clustering, Pattern recognition, 02 engineering and technology, 01 natural sciences, Spectral clustering, 010104 statistics & probability, ComputingMethodologies_PATTERNRECOGNITION, Similarity (network science), Artificial Intelligence, Pattern recognition (psychology), 0202 electrical engineering, electronic engineering, information engineering, Canopy clustering algorithm, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, 0101 mathematics, Cluster analysis, business, Mathematics |
الوصف: | The clustering assumption is to maximize the within-cluster similarity and simultaneously to minimize the between-cluster similarity for a given unlabeled dataset. This paper deals with a new spectral clustering algorithm based on a similarity and dissimilarity criterion by incorporating a dissimilarity criterion into the normalized cut criterion. The within-cluster similarity and the between-cluster dissimilarity can be enhanced to result in good clustering performance. Experimental results on toy and real-world datasets show that the new spectral clustering algorithm has a promising performance. |
تدمد: | 1433-755X 1433-7541 |
URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::e8e6a3ce4b9f3af36d30ece36b270829 https://doi.org/10.1007/s10044-015-0515-x |
حقوق: | CLOSED |
رقم الأكسشن: | edsair.doi...........e8e6a3ce4b9f3af36d30ece36b270829 |
قاعدة البيانات: | OpenAIRE |
تدمد: | 1433755X 14337541 |
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