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From conflue...@apache.org
Subject [CONF] Apache Mahout > Fuzzy K-Means
Date Fri, 29 Jun 2012 15:49:00 GMT
Space: Apache Mahout (https://cwiki.apache.org/confluence/display/MAHOUT)
Page: Fuzzy K-Means (https://cwiki.apache.org/confluence/display/MAHOUT/Fuzzy+K-Means)


Edited by Jeff Eastman:
---------------------------------------------------------------------
Fuzzy K-Means (also called Fuzzy C-Means) is an extension of [K-Means|http://cwiki.apache.org/MAHOUT/k-means.html],
the popular simple clustering technique. While K-Means discovers hard clusters (a point belong
to only one cluster), Fuzzy K-Means is a more statistically formalized method and discovers
soft clusters where a particular point can belong to more than one cluster with certain probability.

h4. Algorithm

Like K-Means, Fuzzy K-Means works on those objects which can be represented in n-dimensional
vector space and a distance measure is defined.
The algorithm is similar to k-means.
* Initialize k clusters
* Until converged
** Compute the probability of a point belong to a cluster for every <point,cluster>
pair
** Recompute the cluster centers using above probability membership values of points to clusters

h4. Design Implementation

The design is similar to K-Means present in Mahout. It accepts an input file containing vector
points. User can either provide the cluster centers as input or can allow canopy algorithm
to run and create initial clusters.

Similar to K-Means, the program doesn't modify the input directories. And for every iteration,
the cluster output is stored in a directory cluster-N. The code has set number of reduce tasks
equal to number of map tasks. So, those many part-0
\\
\* files are created in clusterN directory. The code uses driver/mapper/combiner/reducer as
follows:

FuzzyKMeansDriver - This is similar to&nbsp; KMeansDriver. It iterates over input points
and cluster points for specified number of iterations or until it is converged.During every
iteration i, a new cluster-i directory is created which contains the modified cluster centers
obtained during FuzzyKMeans iteration. This will be feeded as input clusters in the next iteration.&nbsp;
Once Fuzzy KMeans is run for specified number of iterations or until it is converged, a map
task is run to output "the point and the cluster membership to each cluster" pair as final
output to a directory named "points".

FuzzyKMeansMapper - reads the input cluster during its configure() method, then&nbsp;
computes cluster membership probability of a point to each cluster.Cluster membership is inversely
propotional to the distance. Distance is computed using&nbsp; user supplied distance measure.
Output key is encoded clusterId. Output values are ClusterObservations containing observation
statistics.

FuzzyKMeansCombiner - receives all key:value pairs from the mapper and produces partial sums
of the cluster membership probability times input vectors for each cluster. Output key is:
encoded cluster identifier. Output values are ClusterObservations containing observation statistics.

FuzzyKMeansReducer - Multiple reducers receives certain keys and all values associated with
those keys. The reducer sums the values to produce a new centroid for the cluster which is
output. Output key is: encoded cluster identifier (e.g. "C14". Output value is: formatted
cluster identifier (e.g. "C14"). The reducer encodes unconverged clusters with a 'Cn' cluster
Id and converged clusters with 'Vn' clusterId.

h2. Running Fuzzy k-Means Clustering

The Fuzzy k-Means clustering algorithm may be run using a command-line invocation on FuzzyKMeansDriver.main
or by making a Java call to FuzzyKMeansDriver.run(). 

Invocation using the command line takes the form:

{noformat}
bin/mahout fkmeans \
    -i <input vectors directory> \
    -c <input clusters directory> \
    -o <output working directory> \
    -dm <DistanceMeasure> \
    -m <fuzziness argument >1> \
    -x <maximum number of iterations> \
    -k <optional number of initial clusters to sample from input vectors> \
    -cd <optional convergence delta. Default is 0.5> \
    -ow <overwrite output directory if present>
    -cl <run input vector clustering after computing Clusters>
    -e <emit vectors to most likely cluster during clustering>
    -t <threshold to use for clustering if -e is false>
    -xm <execution method: sequential or mapreduce>
{noformat}

*Note:* if the -k argument is supplied, any clusters in the -c directory will be overwritten
and -k random points will be sampled from the input vectors to become the initial cluster
centers.

Invocation using Java involves supplying the following arguments:

# input: a file path string to a directory containing the input data set a SequenceFile(WritableComparable,
VectorWritable). The sequence file _key_ is not used.
# clustersIn: a file path string to a directory containing the initial clusters, a SequenceFile(key,
SoftCluster | Cluster | Canopy). Fuzzy k-Means SoftClusters, k-Means Clusters and Canopy Canopies
may be used for the initial clusters.
# output: a file path string to an empty directory which is used for all output from the algorithm.
# measure: the fully-qualified class name of an instance of DistanceMeasure which will be
used for the clustering.
# convergence: a double value used to determine if the algorithm has converged (clusters have
not moved more than the value in the last iteration)
# max-iterations: the maximum number of iterations to run, independent of the convergence
specified
# m: the "fuzzyness" argument, a double > 1. For m equal to 2, this is equivalent to normalising
the coefficient linearly to make their sum 1. When m is close to 1, then the cluster center
closest to the point is given much more weight than the others, and the algorithm is similar
to k-means.
# runClustering: a boolean indicating, if true, that the clustering step is to be executed
after clusters have been determined.
# emitMostLikely: a boolean indicating, if true, that the clustering step should only emit
the most likely cluster for each clustered point.
# threshold: a double indicating, if emitMostLikely is false, the cluster probability threshold
used for emitting multiple clusters for each point. A value of 0 will emit all clusters with
their associated probabilities for each vector.
# runSequential: a boolean indicating, if true, that the algorithm is to use the sequential
reference implementation running in memory.

After running the algorithm, the output directory will contain:
# clusters-N: directories containing SequenceFiles(Text, SoftCluster) produced by the algorithm
for each iteration. The Text _key_ is a cluster identifier string.
# clusteredPoints: (if runClustering enabled) a directory containing SequenceFile(IntWritable,
WeightedVectorWritable). The IntWritable _key_ is the clusterId. The WeightedVectorWritable
_value_ is a bean containing a double _weight_ and a VectorWritable _vector_ where the weights
are computed as 1/(1+distance) where the distance is between the cluster center and the vector
using the chosen DistanceMeasure. 

h1. Examples

The following images illustrate Fuzzy k-Means clustering applied to a set of randomly-generated
2-d data points. The points are generated using a normal distribution centered at a mean location
and with a constant standard deviation. See the README file in the [/examples/src/main/java/org/apache/mahout/clustering/display/README.txt|http://svn.apache.org/repos/asf/mahout/trunk/examples/src/main/java/org/apache/mahout/clustering/display/README.txt]
for details on running similar examples.

The points are generated as follows:

* 500 samples m=\[1.0, 1.0\] sd=3.0
* 300 samples m=\[1.0, 0.0\] sd=0.5
* 300 samples m=\[0.0, 2.0\] sd=0.1

In the first image, the points are plotted and the 3-sigma boundaries of their generator are
superimposed. 

!SampleData.png!

In the second image, the resulting clusters (k=3) are shown superimposed upon the sample data.
As Fuzzy k-Means is an iterative algorithm, the centers of the clusters in each recent iteration
are shown using different colors. Bold red is the final clustering and previous iterations
are shown in \[orange, yellow, green, blue, violet and gray\]. Although it misses a lot of
the points and cannot capture the original, superimposed cluster centers, it does a decent
job of clustering this data.

!FuzzyKMeans.png!

The third image shows the results of running Fuzzy k-Means on a different data set (see [Dirichlet
Process Clustering] for details) which is generated using asymmetrical standard deviations.
Fuzzy k-Means does a fair job handling this data set as well.

!2dFuzzyKMeans.png!

h4. References&nbsp;

* [http://en.wikipedia.org/wiki/Data_clustering#Fuzzy_c-means_clustering]

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