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.
K-Means background, k-means-commandline, and fuzzy-k-means-commandline.
LDA (Latent Dirichlet Allocation) background and lda-commandline.
TODO: kmeans++/ streaming kMeans documentation
Mahout has a cluster dumper utility that can be used to retrieve and evaluate your clustering data.
./bin/mahout clusterdump <OPTIONS>
--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
(text|sequencefile)
--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
{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}