This paper introduces a measure of corpus homogeneity that indicates the amount of topical dispersion in a corpus. The measure is based on the density of neighborhoods in semantic ...
Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space. Despite significant research, these methods have re...
Maximum margin clustering (MMC) has recently attracted considerable interests in both the data mining and machine learning communities. It first projects data samples to a kernel...
Data streams are often locally correlated, with a subset of streams exhibiting coherent patterns over a subset of time points. Subspace clustering can discover clusters of objects...
Efficient hierarchical architectures for reconfigurable and adaptive multi-agent networks require dynamic cluster formation among the set of nodes (agents). In the absence of cen...