Density Based
Clustering 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.
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:
- Eliminate noise points
- Perform clustering on remaining points
- 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
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