DocumentCode
441762
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
Volume
3
fYear
2005
fDate
18-21 Aug. 2005
Firstpage
1509
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location
Guangzhou, China
Print_ISBN
0-7803-9091-1
Type
conf
DOI
10.1109/ICMLC.2005.1527183
Filename
1527183
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