Relative density based k-nearest neighbors clustering algorithm
Qingbao Liu, Su Deng, LU Chang-hui, Bo Wang, Yong-Feng Zhou · 2004
With strong ability of discovering arbitrary shape clusters and handling noise, density based clustering is one of primary methods for data mining. This paper provides a k-nearest neighbors clustering algorithm based on relative density, which efficiently resolves these problem of being very sensitive to the user-defined parameters and too difficult for users to determine the parameters.