SpecVCMV: Improving cluster visualisation
Sverre Gunnersen, Kate Smith‐Miles, Vincent Chieng Chen Lee · 2011
This paper proposes a new approach to validating and visualising cluster structure by combining fuzzy membership functions and spectral clustering. By modifying the Visual Cluster Validity algorithm (VCV) to use an external fuzzy membership function as the distance measure and using sum of cluster membership as the sorting function, computational experiments on both the Zelnik-Manor synthetic and UCI real datasets show the proposed method, SpecVCMV, more clearly identifies the underlying cluster structure in the data.