Paper Info
Title
An Efficient Clustering Algorithm for 2D Multi-density Dataset in Large Database
Abstract
Spatial clustering is an important component of spatial data mining. The requirement of detecting clusters of points arises in many applications. One of the challenges in spatial clustering is to find clusters on multi-density dataset. In this paper, a grid-based density-confidence-interval clustering algorithm for 2-dimensional multi-density dataset is proposed, called GDCIC. The proposed algorithm combines the density confidence interval with grid-based clustering, and produces accurate density estimation in local areas for local density thresholds. Local dense areas are distinguished from sparse areas or outliers according to these thresholds. Experiments based on both synthetic and real datasets verify that the algorithm is efficiently for multi-data sets and handle outliers effectively.
Year
DOI
Venue
2007
10.1109/MUE.2007.67
MUE
Keywords
Field
DocType
pattern clustering,2d multidensity dataset,density confidence interval,clustering algorithm,grid computing,spatial data mining,local area,accurate density estimation,grid-based clustering,local density threshold,proposed algorithm,local dense area,spatial clustering,local density thresholds,data mining,very large databases,large database,grid-based density-confidence-interval clustering algorithm,efficient clustering algorithm,multi-density dataset,sampling methods,data analysis,density estimation,information science,computer science,confidence interval,clustering algorithms,2 dimensional,application software
Fuzzy clustering,Canopy clustering algorithm,Data mining,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Pattern recognition,Computer science,Artificial intelligence,Cluster analysis,DBSCAN,Single-linkage clustering
Conference
ISBN
Citations 
PageRank 
0-7695-2777-9
1
0.36
References 
Authors
3
3
Name
Order
Citations
PageRank
Ying Xia1125.28
Guoyin Wang22144202.16
Song Gao310.70