Clustering your data

After you’ve done the Quickstart and are familiar with the basics of Mahout, it is time to cluster your own data. See also Wikipedia on cluster analysis for more background.

The following pieces may be useful for in getting started:


For starters, you will need your data in an appropriate Vector format, see Creating Vectors. In particular for text preparation check out Creating Vectors from Text.

Running the Process

Retrieving the Output

Mahout has a cluster dumper utility that can be used to retrieve and evaluate your clustering data.

./bin/mahout clusterdump <OPTIONS>

The cluster dumper options are:

  --help (-h)				   Print out help	
  --input (-i) input			   The directory containing Sequence    
					   Files for the Clusters	    

  --output (-o) output			   The output file.  If not specified,  
					   dumps to the console.

  --outputFormat (-of) outputFormat	   The optional output format to write
					   the results as. Options: TEXT, CSV, or GRAPH_ML		 

  --substring (-b) substring		   The number of chars of the	    
					   asFormatString() to print	

  --pointsDir (-p) pointsDir		   The directory containing points  
 					   sequence files mapping input vectors     					   to their cluster.  If specified, 
					   then the program will output the 
					   points associated with a cluster 

  --dictionary (-d) dictionary		   The dictionary file. 	    

  --dictionaryType (-dt) dictionaryType    The dictionary file type	    

  --distanceMeasure (-dm) distanceMeasure  The classname of the DistanceMeasure.
					   Default is SquaredEuclidean.     

  --numWords (-n) numWords		   The number of top terms to print 

  --tempDir tempDir			   Intermediate output directory

  --startPhase startPhase		   First phase to run

  --endPhase endPhase			   Last phase to run

  --evaluate (-e)			   Run ClusterEvaluator and CDbwEvaluator over the
					   input. The output will be appended to the rest of
					   the output at the end.   

More information on using clusterdump utility can be found here

Validating the Output

{quote} Ted Dunning: A principled approach to cluster evaluation is to measure how well the cluster membership captures the structure of unseen data. A natural measure for this is to measure how much of the entropy of the data is captured by cluster membership. For k-means and its natural L_2 metric, the natural cluster quality metric is the squared distance from the nearest centroid adjusted by the log_2 of the number of clusters. This can be compared to the squared magnitude of the original data or the squared deviation from the centroid for all of the data. The idea is that you are changing the representation of the data by allocating some of the bits in your original representation to represent which cluster each point is in. If those bits aren’t made up by the residue being small then your clustering is making a bad trade-off.

In the past, I have used other more heuristic measures as well. One of the key characteristics that I would like to see out of a clustering is a degree of stability. Thus, I look at the fractions of points that are assigned to each cluster or the distribution of distances from the cluster centroid. These values should be relatively stable when applied to held-out data.

For text, you can actually compute perplexity which measures how well cluster membership predicts what words are used. This is nice because you don’t have to worry about the entropy of real valued numbers.

Manual inspection and the so-called laugh test is also important. The idea is that the results should not be so ludicrous as to make you laugh. Unfortunately, it is pretty easy to kid yourself into thinking your system is working using this kind of inspection. The problem is that we are too good at seeing (making up) patterns. {quote}