Data classification based on supporting data gravity
Junlin Li, Hongguang Fu · 2009
This paper introduces a novel data classification method that is based on the idea of data gravity. Many recent clustering and classification ideas based on data gravity tend to consider data gravity magnitude as decisive factor. They eye data gravity as scalar quantity. Novelly in this paper, data gravity is defined to be a vector, and a vector model is set up to classify data by exploiting the internal structure characteristics among vector points in a class. The proposed method is a nonlinear classification technique that can be applied directly on nonlinear separable data sets without concerning nonlinearity-to-linearity transformation (e.g. kernel transformation) of the data. Experiments have showed the validity and some other useful characteristics of this method.