Title :
A Novel Fast Clustering Algorithm
Author :
Li Xia ; Jiang Sheng-Yi ; Su Xiao-Ke
Author_Institution :
Sch. of Inf., Guangdong Univ. of Foreign Studies, Guangzhou, China
Abstract :
SNN is a shared nearest neighbor based clustering algorithm. It is improved to process the data with categorical attributes and be given a simple and definite method to select threshold of the algorithm. By combine one-pass clustering algorithm with the enhanced SNN clustering algorithm, we present a fast clustering algorithm which can find different sizes, shapes and densities in noisy, high dimensional and large dataset. The time complexity of the presented clustering algorithm is nearly linear with the size of dataset. The experimental results on real datasets and synthetic datasets show that the clustering algorithm is effective, robust and practicable.
Keywords :
pattern clustering; SNN; categorical attributes; enhanced SNN clustering algorithm; fast clustering algorithm; one-pass clustering algorithm; shared nearest neighbor based clustering algorithm; Artificial intelligence; Clustering algorithms; Computational intelligence; Educational institutions; Informatics; Information science; Nearest neighbor searches; Noise shaping; Robustness; Shape; Clustering Analysis; Large Dataset; One-pass Clustering Algorithm; SNN Clustering Algorithm;
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
DOI :
10.1109/AICI.2009.33