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
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
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
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Lower bounds for the partitioning of graphs
IBM Journal of Research and Development
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A new algorithm for clustering documents and words simultaneously has recently been presented. As most spectral clustering algorithms, the prior knowledge of the number of clusters present is required. In this paper, we explore a method based on morphology for determining the number of clusters present in the given dataset for co-clustering documents and words. The proposed method employs some refined feature extraction techniques, which mainly include a VAT (Visual Assessment of Cluster Tendency) image representation of input matrix generated by spectral co-clustering documents and words, and the texture information obtained by filtering the VAT image. The number of clusters present in co-clustering documents and words is finally reported by computing the eigengap of gray-scale matrix of filtered image. Our experimental results show that the proposed method works well in practice.