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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Optimized Data Fusion for k-means Laplacian Clustering
verfasst von : Shi Yu, Léon-Charles Tranchevent, Bart De Moor, Yves Moreau
Erschienen in: Kernel-based Data Fusion for Machine Learning
Verlag: Springer Berlin Heidelberg
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Clustering is a fundamental problem in unsupervised learning and a number of different algorithms and methods have emerged over the years.
k
-means and spectral clustering are two popular methods for clustering analysis.
k
-means (KM) is proposed to cluster attribute-based data into
k
numbers of clusters with the minimal distortion [4, 8]. Another well known method, spectral clustering (SC) [18, 20], is also widely adopted in many applications. Unlike KM, SC is specifically developed for graphs, where the data samples are represented as vertices connected by non-negatively weighted undirected edges. The problem of clustering on graphs belongs to another paradigm than the algorithms based on the distortion measure.