An adaptive density-based clustering algorithm for spatial database with noise

  title={An adaptive density-based clustering algorithm for spatial database with noise},
  author={Daoying Ma and Aidong Zhang},
  journal={Fourth IEEE International Conference on Data Mining (ICDM'04)},
Clustering spatial data has various applications. Several clustering algorithms have been proposed to cluster objects in spatial databases. Spatial object distribution has significant effect on the results of clustering. Few of current algorithms consider the distribution of objects while processing clusters. In this paper, we propose an adaptive density-based clustering algorithm, ADBC, which uses a novel adaptive strategy for neighbor selection based on spatial object distribution to improve… CONTINUE READING
Highly Cited
This paper has 26 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.


Publications citing this paper.
Showing 1-10 of 16 extracted citations

Mitigating False Negative intruder decisions in WSN-based Smart Grid monitoring

2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC) • 2017
View 2 Excerpts

An Improved Fast Search Clustering Algorithm Based on Kernel Density

2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity) • 2015
View 1 Excerpt

Co-clustering of fuzzy lagged data

Knowledge and Information Systems • 2014
View 1 Excerpt


Publications referenced by this paper.
Showing 1-10 of 12 references

Multitarget-Multisensor Tracking: Applications and Advances Volume III

Y. Bar-Shalom, W. D. Blair
Artech House, Norwood, MA • 2000
View 1 Excerpt

Design and Analysis of Modern Tracking Sysytem

S. Blackman, R. Popoli
Artech House, Norwood, MA • 1999
View 1 Excerpt


M. Ester, H.-P. Kriegel
Sander, , and X. Xu. A densitybased algorithm for discovering clusters in large spatial databases. Proc. 1996 Int. Conf. Knowledge Discovery and Data Mining(KDD’96), pages 226–231 • 1996
View 1 Excerpt

Similar Papers

Loading similar papers…