Concept decompositions for large sparse text data using clustering
Machine Learning
Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
Fuzzy C-Means Clustering Algorithm Based on Kernel Method
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
Survey of Text Mining
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Clustering Large Graphs via the Singular Value Decomposition
Machine Learning
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Linearized cluster assignment via spectral ordering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Text Mining: Predictive Methods for Analyzing Unstructured Information
Text Mining: Predictive Methods for Analyzing Unstructured Information
IEEE Transactions on Knowledge and Data Engineering
Cluster Analysis
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
Artificial Intelligence in Medicine
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph nodes clustering with the sigmoid commute-time kernel: A comparative study
Data & Knowledge Engineering
WIT: web people search disambiguation using random walks
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Affinity measures based on the graph Laplacian
TextGraphs-3 Proceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing
Interior distance using barycentric coordinates
SGP '09 Proceedings of the Symposium on Geometry Processing
ACM Transactions on Graphics (TOG)
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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This work presents a kernel method for clustering the nodes of a weighted, undirected, graph. The algorithm is based on a two-step procedure. First, the sigmoid commute-time kernel (KCT), providing a similarity measure between any couple of nodes by taking the indirect links into account, is computed from the adjacency matrix of the graph. Then, the nodes of the graph are clustered by performing a kernel kmeans or fuzzy k-means on this CT kernel matrix. For this purpose, a new, simple, version of the kernel k-means and the kernel fuzzy k-means is introduced. The joint use of the CT kernel matrix and kernel clustering appears to be quite successful. Indeed, it provides good results on a document clustering problem involving the newsgroups database.