The diameter k-clustering problem is the problem of partitioning a finite subset of Rd into k subsets called clusters such that the maximum diameter of the clusters is minimized. ...
This paper proposes a novel visualization approach, which can depict the variations between different human motion data. This is achieved by representing the time dimension of eac...
In this paper, an extension of a dimensionality reduction algorithm called NONNEGATIVE MATRIX FACTORIZATION is presented that combines both `bag of words' data and syntactic ...
Background: The expansion of automatic imaging technologies has created a need to be able to efficiently compare and review large sets of image data. To enable comparisons of imag...
— Network clustering enables us to view a complex network at the macro level, by grouping its nodes into units whose characteristics and interrelationships are easier to analyze ...