LD-BSCA: A local-density based spatial clustering algorithm

  title={LD-BSCA: A local-density based spatial clustering algorithm},
  author={Guiyi Wei and Haiping Liu},
  journal={2009 IEEE Symposium on Computational Intelligence and Data Mining},
Density-based clustering algorithms are very powerful to discover arbitrary-shaped clusters in large spatial databases. However, in many cases, varied local-density clusters exist in different regions of data space. In this paper, a new algorithm LD-BSCA is proposed with introducing the concept of local MinPts (a minimum number of points) and the new cluster expanding condition: ExpandConClId (Expanding Condition of ClId-th Cluster). We minimize the algorithm input down to only one parameter… CONTINUE READING

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Publications referenced by this paper.
Showing 1-10 of 18 references

A robust fuzzy clustering method based on local density for automatic spike sorting

W. Ding, J. Yuan
A density - based fuzzy clustering technique for non - destructive detection of defects in materials , NDT & E International • 2007

A Local Density Based Spatial Clustering Algorithm with Noise

2006 IEEE International Conference on Systems, Man and Cybernetics • 2006

An efficient approach to clustering in large multimedia databases with noise

R. Agrawal, P. E. Stolorz, G. Piatetsky-Shapiro
Proceedings of Fourth International Conference on Knowledge Discovery and Data Mining • 1998

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