Title :
Grid-based clustering algorithm for multi-density
Author :
Qiu, Bao-Zhi ; ZHANG, Xi-ZHI ; Shen, Jun-Yi
Author_Institution :
Sch. of Electron. & Inf. Eng., Xi´´an Jiaotong Univ., China
Abstract :
This paper presents a grid-based clustering algorithm for multi-density (GDD). The GDD is a kind of the multi-stage clustering that integrates grid-based clustering, the technique of density threshold descending and border points extraction. Scanning the dataset only once, the GDD can discover clusters of arbitrary shapes. The experiment results show that it can discover outliers or noises effectively and get good cluster quality for multi-data sets.
Keywords :
data mining; grid computing; pattern clustering; border point extraction; density threshold descending; grid-based clustering algorithm; multi-density; multi-stage clustering; Clustering algorithms; Data mining; Geometry; Grid computing; Multi-stage noise shaping; Nearest neighbor searches; Noise figure; Partitioning algorithms; Shape; Unsupervised learning; Border points extraction; Density threshold descending; Multi-stage clustering;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527183
