Adaptive clustering based on local neighborhood interactions
Markus Anderle, Michael J. L. Kirby · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
We propose a clustering algorithm that dynamically inserts and relocates cluster units based only on their interaction with neighboring clusters and data points. This leads to update and allocation procedures for centers locations based on local data distributions. These local data distributions can be uncovered by examining neighboring clusters or local interconnections between center locations. The consequence of only adapting nearest centers to a newly inserted cluster unit is a significant reduction in the necessary computational power for finding the center distribution that reduces the global distortion error. The proposed algorithm inserts new cluster units based on local distortion errors and utility measures, and uses a local LBG routine to integrate the new unit. Experiments have shown that it is not necessary to let the LBG routine converge in order to achieve integration of the new unit; the number of necessary iterations is instead determined by the center distribution in the neighborhood of new units. The algorithm thus offers a considerable speedup compared to conventional clustering algorithms that take the entire data set into account when inserting or relocating cluster units.