Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Model-Based Halftoning for Color Image Segmentation
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Generative model-based clustering of directional data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-Supervised Classification Using Linear Neighborhood Propagation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Spectral Methods for Automatic Multiscale Data Clustering
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
IEEE Transactions on Knowledge and Data Engineering
A tutorial on spectral clustering
Statistics and Computing
Robust self-tuning semi-supervised learning
Neurocomputing
Approximate Spectral Clustering
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
On spatial quantization of color images
IEEE Transactions on Image Processing
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The construction process for a similarity matrix has an important impact on the performance of spectral clustering algorithms. In this paper, we propose a random walk based approach to process the Gaussian kernel similarity matrix. In this method, the pair-wise similarity between two data points is not only related to the two points, but also related to their neighbors. As a result, the new similarity matrix is closer to the ideal matrix which can provide the best clustering result. We give a theoretical analysis of the similarity matrix and apply this similarity matrix to spectral clustering. We also propose a method to handle noisy items which may cause deterioration of clustering performance. Experimental results on real-world data sets show that the proposed spectral clustering algorithm significantly outperforms existing algorithms.