Multilevel hypergraph partitioning: application in VLSI domain
DAC '97 Proceedings of the 34th annual Design Automation Conference
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
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
Ensembling neural networks: many could be better than all
Artificial Intelligence
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
Finding Consistent Clusters in Data Partitions
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Clustering Using Evidence Accumulation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensembles of Partitions via Data Resampling
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Analysis of Consensus Partition in Cluster Ensemble
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Clustering Ensemble Based on Dual Boosting
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Fuzzy clustering ensemble based on mutual information
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Clustering aggregation by probability accumulation
Pattern Recognition
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Image segmentation with ratio cut
IEEE Transactions on Pattern Analysis and Machine Intelligence
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An unsupervised learning algorithm, named soft spectral clustering ensemble (SSCE), is proposed in this paper. Until now many proposed ensemble algorithms cannot be used on image data, even images of a mere 256 脳 256 pixels are too expensive in computational cost and storage. The proposed method is suitable for performing image segmentation and can, to some degree, solve some open problems of spectral clustering (SC). In this paper, a random scaling parameter and Nyström approximation are applied to generate the individual spectral clusters for ensemble learning. We slightly modify the standard SC algorithm to aquire a soft partition and then map it via a centralized logcontrast transform to relax the constraint of probability data, the sum of which is one. All mapped data are concatenated to form the new features for each instance. Principal component analysis (PCA) is used to reduce the dimension of the new features. The final aggregated result can be achieved by clustering dimension-reduced data. Experimental results, on UCI data and different image types, show that the proposed algorithm is more efficient compared with some existing consensus functions.