Enhanced Density Based Algorithm for Clustering Large Datasets

  • Yasser El-Sonbaty
  • Hany Said
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 57)

Summary

Clustering is one of the data mining techniques that extracts knowledge from spatial datasets. DBSCAN algorithm was considered as well-founded algorithm as it discovers clusters in different shapes and handles noise effectively. There are several algorithms that improve DBSCAN as fast hybrid density algorithm (L-DBSCAN) and fast density-based clustering algorithm. In this paper, an enhanced algorithm is proposed that improves fast density-based clustering algorithm in the ability to discover clusters with different densities and clustering large datasets.

Keywords

Distance Threshold Cluster Scheme Cluster Validity Dist Graph Region Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yasser El-Sonbaty
    • 1
  • Hany Said
    • 1
  1. 1.Arab Academy for Science & TechnologyEgypt

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