A novel kernel method for clustering

By Francesco Camastra, Alessandro Verri. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005

351 citations recorded by the source corpus.

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Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study (2010, methodology): “Kernel-based clustering techniques based on K-Means clusters have been developed in [7,9,16].”
A review of machine learning approaches to Spam filtering (2009, background): “Spam filtering is also increasingly considered as a benchmark for testing newly developed machine learning algorithms, not specifically designed for this problem (e.g. Camastra & Verri, 2005; Gadat & Younes, 2007; Qin & Zhang, 2008).”
A survey of kernel and spectral methods for clustering (2008, methodology): “[17] [21] [24]) have been applied to standard benchmarks such as Ionosphere [25], Breast Cancer [26] and Iris [27].”
Multiple Kernel Fuzzy Clustering (2012, methodology): “Camastra and Verri [22] presented a kernel-based clustering algorithm that is inspired by the k-means algorithm that iteratively refines results using a one-class support vector machine.”
Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling (2008, background): “However, how to select appropriate parameters for kernel functions so as to improve the generalization performance remains an open problem for most kernel machine models [23,64,102,150–152].”
Kernel Methods and Machine Learning (2014, methodology): “Many kernel-based K-means clustering methods have been proposed in the literature [31, 85, 305, 317, 319].”