Title :
Relative density based k-nearest neighbors clustering algorithm
Author :
Liu, Qing-Bao ; Deng, Su ; Lu, Chang-hui ; Wang, Bo ; Zhou, Yong-feng
Author_Institution :
Dept. of Manage. Sci. & Eng., Nat. Univ. of Defense Technol., Hunan, China
Abstract :
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.
Keywords :
data mining; density; noise; parameter estimation; pattern clustering; arbitrary shape clusters; clustering parameter; data mining; noise handling; relative density based k-nearest neighbors clustering algorithm; Clustering algorithms; Data analysis; Data engineering; Data mining; Engineering management; Humans; Noise shaping; Pattern recognition; Shape; Technology management;
Conference_Titel :
Machine Learning and Cybernetics, 2003 International Conference on
Print_ISBN :
0-7803-8131-9
DOI :
10.1109/ICMLC.2003.1264457
