أعرض في EDS

Spectral clustering based on similarity and dissimilarity criterion

التفاصيل البيبلوغرافية
العنوان: Spectral clustering based on similarity and dissimilarity criterion
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
الوصف
تدمد:1433755X
14337541