Examples of weka.clusterers.MakeDensityBasedClusterer

weka.clusterers.MakeDensityBasedClusterer

Class for wrapping a Clusterer to make it return a distribution and density. Fits normal distributions and discrete distributions within each cluster produced by the wrapped clusterer. Supports the NumberOfClustersRequestable interface only if the wrapped Clusterer does.

Valid options are:

 -M <num> minimum allowable standard deviation for normal density computation  (default 1e-6)

 -W <clusterer name> Clusterer to wrap. (default weka.clusterers.SimpleKMeans)

  Options specific to clusterer weka.clusterers.SimpleKMeans:

 -N <num> number of clusters. (default 2).

 -V Display std. deviations for centroids.

 -M Replace missing values with mean/mode.

 -S <num> Random number seed. (default 10)

Options after "--" are passed on to the base clusterer. @author Richard Kirkby (rkirkby@cs.waikato.ac.nz) @author Mark Hall (mhall@cs.waikato.ac.nz) @author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 5538 $

    instances = Filter.useFilter(instances, m_standardize);


    SimpleKMeans sk = new SimpleKMeans();
    sk.setNumClusters(m_numClusters);
    sk.setSeed(m_clusteringSeed);
    MakeDensityBasedClusterer dc = new MakeDensityBasedClusterer();
    dc.setClusterer(sk);
    dc.setMinStdDev(m_minStdDev);
    m_basisFilter = new ClusterMembership();
    m_basisFilter.setDensityBasedClusterer(dc);
    m_basisFilter.setInputFormat(instances);
    Instances transformed = Filter.useFilter(instances, m_basisFilter);

Examples of weka.clusterers.MakeDensityBasedClusterer

Related Classes of weka.clusterers.MakeDensityBasedClusterer