CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering.
Hyeon JeonGhulam Jilani QuadriHyunwook LeePaul RosenDanielle Albers SzafirJinwook SeoPublished in: CoRR (2023)
Keyphrases
- clustering algorithm
- intra cluster
- inter cluster
- data clustering
- hierarchical clustering
- cluster analysis
- overlapping clusters
- clustering method
- clustering approaches
- cluster centers
- k means
- clustering framework
- visual perception
- low level
- density based clustering algorithm
- clustering result
- agglomerative hierarchical clustering
- unsupervised clustering
- clustering procedure
- disjoint clusters
- cluster validation
- document clustering
- subspace clustering
- human vision
- similar objects
- supervised clustering
- cluster membership
- constrained clustering
- high level
- similarity measure
- clustering quality
- categorical data
- dissimilarity measure
- data points
- high dimensional data
- visual information
- visual processing
- overlapping clustering
- human visual
- model based clustering
- visual features
- similarity matrix
- instance level constraints
- visual stimuli
- spectral clustering
- clustering scheme
- fuzzy clustering
- hierarchical clustering algorithm
- cluster labels
- data objects
- human visual system
- cluster validity
- distance measure
- evolutionary clustering
- validity measures
- empirically derived
- arbitrary shape
- rough k means
- semi supervised clustering