DGCL: an efficient density and grid based clustering algorithm for large spatial database

Authors:
Ho Seok Kim;Song Gao;Ying Xia;Gyoung Bae Kim;Hae Young Bae
Affiliations:
Department of Computer Science and Information Engineering, Inha University, Incheon, Korea;College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Nan'an Distinct, ChongQing City, P.R. China;College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Nan'an Distinct, ChongQing City, P.R. China;Department of Computer Education, Seowon University, Chungbuk, Korea;Department of Computer Science and Information Engineering, Inha University, Incheon, Korea
Venue:
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
Year:
2006

Citing 5
Cited 2

Data mining: concepts and techniques

Data mining: concepts and techniques
GraphZip: a fast and automatic compression method for spatial data clustering

Proceedings of the 2004 ACM symposium on Applied computing
FAÇADE: a fast and effective approach to the discovery of dense clusters in noisy spatial data

SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Clustering High-Dimensional Data with Low-Order Neighbors

WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
An Efficient Density Based Clustering Algorithm for Large Databases

ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence

DGDCT: a distributed grid-density based algorithm for intrinsic cluster detection over massive spatial data

ICDCN'08 Proceedings of the 9th international conference on Distributed computing and networking
A grid-density based technique for finding clusters in satellite image

Pattern Recognition Letters

Quantified Score

Hi-index	0.01

Visualization

Abstract

Spatial clustering, which groups similar objects based on their distance, connectivity, or their relative density in space, is an important component of spatial data mining. Clustering large data sets has always been a serious challenge for clustering algorithms, because huge data set makes the clustering process extremely costly. In this paper, we propose DGCL, an enhanced Density-Grid based Clustering algorithm for Large spatial database. The characteristics of dense area can be enhanced by considering the affection of the surrounding area. Dense areas are analytically identified as clusters by removing sparse area or outliers with the help of a density threshold. Synthetic datasets are used for testing and the result shows the superiority of our approach.