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12 April 2021 Determination of the number of clusters by symmetric non-negative matrix factorization
Raviteja Vangara, Kim Ø. Rasmussen, Gopinath Chennupati, Boian Alexandrov
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Proceedings Volume 11730, Big Data III: Learning, Analytics, and Applications; 117300G (2021) https://doi.org/10.1117/12.2588456
Event: SPIE Defense + Commercial Sensing, 2021, Online Only
Abstract
Clustering is an unsupervised machine learning technique that serves to extract patterns in unlabeled datasets by grouping their elements based on a similarity measure. A priori knowledge of the number of clusters is needed in most of the clustering techniques, which is both difficult and necessary for an effective and accurate pattern recognition and latent (not directly observable) feature analysis. Recently, graph based Symmetric Non-negative Matrix factorization (SymmNMF) has been demonstrated to perform better than k-means and spectral clustering. Here, we present a consensus clustering based on robust resampling technique which in conjunction with SymmNMF and Proportion of Ambiguous Clustering (PAC) criterion performs a robust graphical clustering and accurate identification of the number of clusters in several non-convex benchmark datasets.
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Raviteja Vangara, Kim Ø. Rasmussen, Gopinath Chennupati, and Boian Alexandrov "Determination of the number of clusters by symmetric non-negative matrix factorization", Proc. SPIE 11730, Big Data III: Learning, Analytics, and Applications, 117300G (12 April 2021); https://doi.org/10.1117/12.2588456
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KEYWORDS
Picture Archiving and Communication System
Pattern recognition
Data analysis
Data mining
Machine learning
Spherical lenses
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