منابع مشابه
Consistency of Spectral Clustering
Consistency is a key property of all statistical procedures analyzing randomly sampled data. Surprisingly, despite decades of work, little is known about consistency of most clustering algorithms. In this paper we investigate consistency of the popular family of spectral clustering algorithms, which clusters the data with the help of eigenvectors of graph Laplacian matrices. We develop new meth...
متن کاملA variational approach to the consistency of spectral clustering
This paper establishes the consistency of spectral approaches to data clustering. We consider clustering of point clouds obtained as samples of a ground-truth measure. A graph representing the point cloud is obtained by assigning weights to edges based on the distance between the points they connect. We investigate the spectral convergence of both unnormalized and normalized graph Laplacians to...
متن کاملSpectral Clustering: Advanced Clustering Techniques
Clustering is one of the widely using data mining technique that is used to place data elements into allied groups of “similar behavior”. The conventional clustering algorithm called K-Means algorithm has some well-known problems, i.e., it does not work properly on clusters with not well defined centers, it is difficult to choose the number of clusters to construct different initial centers can...
متن کاملLimits of Spectral Clustering
An important aspect of clustering algorithms is whether the partitions constructed on finite samples converge to a useful clustering of the whole data space as the sample size increases. This paper investigates this question for normalized and unnormalized versions of the popular spectral clustering algorithm. Surprisingly, the convergence of unnormalized spectral clustering is more difficult t...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2008
ISSN: 0090-5364
DOI: 10.1214/009053607000000640