Quantitative measures of change based on feature organization: eigenvalues and eigenvectors
Computer Vision and Image Understanding
On clusterings: Good, bad and spectral
Journal of the ACM (JACM)
Clustering Large Graphs via the Singular Value Decomposition
Machine Learning
Beyond streams and graphs: dynamic tensor analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A tutorial on spectral clustering
Statistics and Computing
Text classification by aggregation of SVD eigenvectors
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
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Clustering of high dimensional data is often performed by applying Singular Value Decomposition (SVD) on the original data space and building clusters from the derived eigenvectors. Often no single eigenvector separates the clusters. We propose a method that combines the self-similarity matrices of the eigenvector in such a way that the concepts are well separated. We compare it with a K-Means approach on public domain data sets and discuss when and why our method outperforms the K-Means on SVD method.