Advertisement

The Algorithm of DBSCAN Based on Probability Distribution

  • Ma Yu
  • Gao Yuling
  • Song Shaoyun
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)

Abstract

Data cluster is an important area of data mining and this technology has been vastly applied in many fields like data mining, statistical data analysis, mode recognition and image processing. Up to now, many cluster calculation methods that are applied to large-scale datbase have been put forward. The algorithm of DBSCAN is the spatial cluster method based on density with the advantages of fast-speed, effectiveness in dealing with noise and finding out clusters of any shape. Aimed at the limitations of DBSCAN in dealing with non-core object, this paper puts forward the algorithm of DBSCAN based on probability distribution. The results shows that the improved algorithm has improved the quality of cluster.

Keywords

The algorithm of DBSCAN Cluster algorithm Probability Data mining 

References

  1. 1.
    Han J, Kamber M (2001) Concept and technology of data mining translated by fang ming, meng xiaofeng. Machine Press, BeijingGoogle Scholar
  2. 2.
    Ester M, Kriegel HP, Sander J, Xu XA (1996) Density-based algorithm for discovering clusters in large spatial databases with. In: Proceedings second international conference knowledge discovery and data mining, AAAI Press, PortlandGoogle Scholar
  3. 3.
    Zhou S, Fangye, Zhou A (2000) DBSACAN algorithm based on the data extraction. Micro-Comput Syst 21(12):1270–1274Google Scholar
  4. 4.
    Zhou S, Zhou A, Caojin (2000) DBSACAN algorithm based on the data distribution. Comput Res Dev 37(10):1153–1159Google Scholar
  5. 5.
    Zhou S, Zhou A, Caojin, Hu Y (2000) Algorithm of speedy cluster based on density. Comput Res Dev. 37(11):1287–1292Google Scholar
  6. 6.
    Dempster AP, Larid NM, Rubin DB (1997) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B 39(1):1–38Google Scholar
  7. 7.
    Tan P-N, Steinbach MC, Kumar V (2006) Introduction of data mining translated by fangming, fang Jianhong. People’s Post Press, BeijingGoogle Scholar
  8. 8.
    Lin C-R, Chen M-S (2005) Combining partitional and hierarchical alorithms for robust and efficient data clustering with cohesion self-merging. IEEE Trans Knowl Data Eng 17(2):145–159Google Scholar
  9. 9.
    Gen S, Zhang L (2001) Probability statistics (second version). Beijing University Press, BeijingGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.School of Information Technology and EngineeringYuxi Normal UniversityYuxiChina

Personalised recommendations