[CONF] Apache Lucene Mahout: Canopy Clustering (page created)

[co...@apache.org]

Canopy Clustering (MAHOUT) created by Jeff Eastman
   http://cwiki.apache.org/confluence/display/MAHOUT/Canopy+Clustering

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h1. Canopy Clustering

Canopy Clustering is a very simple, fast and surprisingly accurate method for grouping objects into clusters. All objects are represented as a point in a multidimensional feature space. The algorithm uses a fast approximate distance metric and two distance thresholds T1 > T2 for processing. The basic algorithm is to begin with a set of points and remove one at random. Create a Canopy containing this point and iterate through the remainder of the point set. At each point, if its distance from the first point is < T1, then add the point to the cluster. If, in addition, the distance is < T2, then remove the point from the set. This way points that are very close to the original will avoid all further processing. The algorithm loops until the initial set is empty, accumulating a set of Canopies, each containing one or more points. A given point may occur in more than one Canopy.

Canopy Clustering is often used as an initial step in more rigorous clustering techniques, such as [k-Means]. By starting with an initial clustering the number of more expensive distance measurements can be significantly reduced by ignoring points outside of the initial canopies.

h2. Strategy for parallelization

Looking at the sample Hadoop implementation in [http://code.google.com/p/canopy-clustering/] the processing is done in 2 M/R steps:
# The data is massaged into suitable input format
# Each mapper performs canopy clustering on the points in its input set and outputs its canopies
# The reducer clusters the canopy centers and merges the points

Some ideas can be found in [Cluster computing and MapReduce|http://code.google.com/edu/content/submissions/mapreduce-minilecture/listing.html] lecture video series \[by Google(r)\]; Canopy Clustering is discussed in [lecture #4|http://www.youtube.com/watch?v=1ZDybXl212Q]. Slides can be found [here|http://code.google.com/edu/content/submissions/mapreduce-minilecture/lec4-clustering.ppt].

h2. Design of implementation

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