Saturday, May 11, 2013

DBSCAN Algorithm Implementation in MATLAB

Density Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm locates regions of high density that are separated from one another by regions of low density. DBSCAN is a center based approach to clustering in which density is estimated for a particular point in the data set by counting the number of points within the specified radius, ɛ, of that point.
The center based approach to density allows us to classify a point as one of the three:

     Core points: These points are in the interior of the dense region
   Border points:These points are not the core points, but fall within the neighborhood of the core points
   Noise points: A noise point is a point that is neither a core point nor a border point.
            The formal definition of DBSCAN algorithm is illustrated below:

      1.        Eliminate noise points
      2.        Perform clustering on remaining points
      3.        current_cluster_label := 0
      ·         for all core points do
      ·         If the core point has no cluster_label then
      current_cluster_label := current_cluster_label +1
      Assign the current core point the current_cluster_label
      ·         end if
      ·         For all points within the radius do
      ·         If the point does not have a cluster_label then
      Label the point with the current_cluster_label
      ·         end if
      ·         end for
      ·         end for

      For Implentation in MATLAB

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