Now showing items 1-5 of 5

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      A dynamic linkage clustering using KD-tree.

      Abudalfa, Shadi; Mikki, Mohammad (2013)
      Some clustering algorithms calculate connectivity of each data point to its cluster by depending on density reachability. These algorithms can find arbitrarily shaped clusters, but they require parameters that are mostly ...
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      A dynamic method for discovering density varied clusters

      Elbatta, Mohammed TH; Ashour, Wesam M. (Hindawi Limited, 2013)
      Density-based spatial clustering of applications with noise (DBSCAN) is a base algorithm for density based clustering. It can find out the clusters of different shapes and sizes from a large amount of data, which is ...
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      Multi density DBSCAN

      Ashour, Wesam M.; Sunoallah, Saad (Springer, Berlin, Heidelberg, 2011)
      Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal ...
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      MULTI-DENSITY DBSCAN USING REPRESENTATIVES: MDBSCAN-UR.

      Ahmed, Rwand; Elzaza, Eman; Ashour, Wesam M. (2011)
      DBSCAN is one of the most popular algorithms for cluster analysis. It can discover clusters with arbitrary shape and separate noises. But this algorithm cannot choose its parameter according to distributing of dataset. It ...
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      New Density-Based Clustering Technique: GMDBSCAN-UR.

      Alhanjouri, Mohammed A.; Ahmed, Rwand D (2012)
      Density Based Spatial Clustering of Applications of Noise (DBSCAN) is one of the most popular algorithms for cluster analysis. It can discover clusters with arbitrary shape and separate noises. But this algorithm cannot ...