Examples of weka.filters.unsupervised.attribute.ClusterMembership

weka.filters.unsupervised.attribute.ClusterMembership

A filter that uses a density-based clusterer to generate cluster membership values; filtered instances are composed of these values plus the class attribute (if set in the input data). If a (nominal) class attribute is set, the clusterer is run separately for each class. The class attribute (if set) and any user-specified attributes are ignored during the clustering operation

Valid options are:

 -W <clusterer name> Full name of clusterer to use. eg: weka.clusterers.EM Additional options after the '--'. (default: weka.clusterers.EM)

 -I <att1,att2-att4,...> The range of attributes the clusterer should ignore. (the class attribute is automatically ignored)

Options after the -- are passed on to the clusterer. @author Mark Hall (mhall@cs.waikato.ac.nz) @author Eibe Frank @version $Revision: 1.16 $

    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);


    if (instances.classAttribute().isNominal()) {

Examples of weka.filters.unsupervised.attribute.ClusterMembership

Related Classes of weka.filters.unsupervised.attribute.ClusterMembership