Kernel Overlapping K-Means for Clustering in Feature Space.
Chiheb-Eddine Ben N'cirNadia EssoussiPatrice BertrandPublished in: KDIR (2010)
Keyphrases
- k means
- feature space
- clustering algorithm
- kernel function
- spectral clustering
- clustering method
- data clustering
- kernel pca
- kernel methods
- kernel based clustering
- cluster analysis
- high dimensionality
- hierarchical clustering
- data points
- feature vectors
- dot product
- self organizing maps
- rough k means
- kernel matrix
- unsupervised clustering
- class separability
- support vector machine
- feature selection
- document clustering
- fuzzy k means
- high dimensional feature space
- squared euclidean distance
- input space
- clustering quality
- kernel space
- clustering approaches
- feature extraction
- low dimensional
- cluster centers
- clustering analysis
- normalized cut
- agglomerative hierarchical clustering
- mean shift
- linear discriminant analysis
- kernel trick
- clustering framework
- principal component analysis
- gaussian kernels
- clustering ensemble
- constrained clustering
- similarity function
- hyperplane
- text clustering
- linear separability
- initial cluster centers
- image representation
- generalized discriminant analysis
- dimensionality reduction
- input data
- feature set
- polynomial kernels
- cluster structure
- clustering result
- support vector
- semi supervised clustering
- metric learning
- dissimilarity measure