Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
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
Outlier Detection Using Random Walks
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
A tutorial on spectral clustering
Statistics and Computing
New spectral methods for ratio cut partitioning and clustering
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Spectral clustering with discriminant cuts
Knowledge-Based Systems
A sample-based hierarchical adaptive K-means clustering method for large-scale video retrieval
Knowledge-Based Systems
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In recent years, spectral clustering has become quite popular for data analysis because it can be solved efficiently by standard linear algebra tools and do not suffer from the problem of local optima. The clustering effect by using such spectral method, however, depends heavily on the description of similarity between instances of the datasets. In this paper, we defined the adjustable line segment length which can adjust the distance in regions with different density. It squeezes the distances in high density regions while widen them in low density regions. And then a density sensitive distance measure satisfied by symmetric, non-negative, reflexivity and triangle inequality was present, by which we can define a new similarity function for spectral clustering. Experimental results show that compared with conventional Euclidean distance based and Gaussian kernel function based spectral clustering, our proposed algorithm with density sensitive similarity measure can obtain desirable clusters with high performance on both synthetic and real life datasets.