Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Path-Based Spectral Clustering with Application to Image Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Spectral clustering and its use in bioinformatics
Journal of Computational and Applied Mathematics
Learning Spectral Clustering, With Application To Speech Separation
The Journal of Machine Learning Research
A tutorial on spectral clustering
Statistics and Computing
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Spectral clustering of biological sequence data
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
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In this study we propose a systematic methodology for constructing a sparse affinity matrix to be used in an advantageous spectral clustering approach. Newton's equations of motion are employed to concentrate the data points around their cluster centers, using an appropriate potential. During this process possibly overlapping clusters are separated, and simultaneously, useful similarity information is gained leading to the enrichment of the affinity matrix. The method was further developed to treat high-dimensional data with application to document clustering. We have tested the method on several benchmark data sets and we witness a superior performance in comparison with the standard approach.