A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
ACM Computing Surveys (CSUR)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
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
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A tutorial on spectral clustering
Statistics and Computing
Improved Nyström low-rank approximation and error analysis
Proceedings of the 25th international conference on Machine learning
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Finding image exemplars using fast sparse affinity propagation
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Density-weighted nyström method for computing large kernel eigensystems
Neural Computation
Fast approximate spectral clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Lower bounds for the partitioning of graphs
IBM Journal of Research and Development
Fast density-weighted low-rank approximation spectral clustering
Data Mining and Knowledge Discovery
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Graph dual regularization non-negative matrix factorization for co-clustering
Pattern Recognition
Fast semi-supervised clustering with enhanced spectral embedding
Pattern Recognition
Integrating Spectral Kernel Learning and Constraints in Semi-Supervised Classification
Neural Processing Letters
Deflation-based power iteration clustering
Applied Intelligence
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In this paper, we propose a novel Fast Affinity Propagation clustering approach (FAP). FAP simultaneously considers both local and global structure information contained in datasets, and is a high-quality multilevel graph partitioning method that can implement both vector-based and graph-based clustering. First, a new Fast Sampling algorithm (FS) is proposed to coarsen the input sparse graph and choose a small number of final representative exemplars. Then a density-weighted spectral clustering method is presented to partition those exemplars on the global underlying structure of data manifold. Finally, the cluster assignments of all data points can be achieved through their corresponding representative exemplars. Experimental results on two synthetic datasets and many real-world datasets show that our algorithm outperforms the state-of-the-art original affinity propagation and spectral clustering algorithms in terms of speed, memory usage, and quality on both vector-based and graph-based clustering.