Vector result = new Vector(4);  result.addElement(new Option( "\tmaximum number of overall iterations\n" + "\t(default 1).", "I", 1, "-I <num>"));  result.addElement(new Option( "\tmaximum number of iterations in the kMeans loop in\n" + "\tthe Improve-Parameter part \n"+ "\t(default 1000).", "M", 1, "-M <num>"));  result.addElement(new Option( "\tmaximum number of iterations in the kMeans loop\n" + "\tfor the splitted centroids in the Improve-Structure part \n"+ "\t(default 1000).", "J", 1, "-J <num>"));  result.addElement(new Option( "\tminimum number of clusters\n" + "\t(default 2).", "L", 1, "-L <num>"));  result.addElement(new Option( "\tmaximum number of clusters\n" + "\t(default 4).", "H", 1, "-H <num>"));  result.addElement(new Option( "\tdistance value for binary attributes\n" + "\t(default 1.0).", "V", 1, "-V <value>"));  result.addElement(new Option( "\tFull class name of KDTree class to use, followed\n" + "\tby scheme options.\n" + "\teg: \"weka.core.KDTree -P\"\n" + "\t(default no KDTree class used).", "K", 1, "-K <KDTree class specification>"));  result.addElement(new Option( "\tcutoff factor, takes the given percentage of the splitted \n" + "\tcentroids if none of the children win\n" + "\t(default 0.0).", "C", 1, "-C <value>"));  result.addElement(new Option( "\tFull class name of Distance function class to use, followed\n" + "\tby scheme options.\n" + "\teg: \"weka.core.MahalanobisDistance\"\n" + "\t(default weka.core.EuclideanDistance).", "D", 1, "-D <distance function class specification>"));  result.addElement(new Option( "\tfile to read starting centers from (ARFF format).", "N", 1, "-N <file name>"));  result.addElement(new Option( "\tfile to write centers to (ARFF format).", "O", 1, "-O <file name>"));  Enumeration en = super.listOptions(); while (en.hasMoreElements()) result.addElement(en.nextElement());  return result.elements();
Vector result = new Vector();  result.addElement(new Option( "\tmaximum number of overall iterations\n" + "\t(default 1).", "I", 1, "-I <num>"));  result.addElement(new Option( "\tmaximum number of iterations in the kMeans loop in\n" + "\tthe Improve-Parameter part \n" + "\t(default 1000).", "M", 1, "-M <num>"));  result.addElement(new Option( "\tmaximum number of iterations in the kMeans loop\n" + "\tfor the splitted centroids in the Improve-Structure part \n" + "\t(default 1000).", "J", 1, "-J <num>"));  result.addElement(new Option( "\tminimum number of clusters\n" + "\t(default 2).", "L", 1, "-L <num>"));  result.addElement(new Option( "\tmaximum number of clusters\n" + "\t(default 4).", "H", 1, "-H <num>"));  result.addElement(new Option( "\tdistance value for binary attributes\n" + "\t(default 1.0).", "B", 1, "-B <value>"));  result.addElement(new Option( "\tUses the KDTree internally\n" + "\t(default no).", "use-kdtree", 0, "-use-kdtree"));  result.addElement(new Option( "\tFull class name of KDTree class to use, followed\n" + "\tby scheme options.\n" + "\teg: \"weka.core.KDTree -P\"\n" + "\t(default no KDTree class used).", "K", 1, "-K <KDTree class specification>"));  result.addElement(new Option( "\tcutoff factor, takes the given percentage of the splitted \n" + "\tcentroids if none of the children win\n" + "\t(default 0.0).", "C", 1, "-C <value>"));  result.addElement(new Option( "\tFull class name of Distance function class to use, followed\n" + "\tby scheme options.\n" + "\t(default weka.core.EuclideanDistance).", "D", 1, "-D <distance function class specification>"));  result.addElement(new Option( "\tfile to read starting centers from (ARFF format).", "N", 1, "-N <file name>"));  result.addElement(new Option( "\tfile to write centers to (ARFF format).", "O", 1, "-O <file name>"));  result.addElement(new Option( "\tThe debug level.\n" + "\t(default 0)", "U", 1, "-U <int>"));  result.addElement(new Option( "\tThe debug vectors file.", "Y", 1, "-Y <file name>"));  Enumeration en = super.listOptions(); while (en.hasMoreElements()) result.addElement(en.nextElement());  return result.elements();
  public Enumeration listOptions() {    Vector result = new Vector(4);     result.addElement(new Option(       "\tmaximum number of overall iterations\n" +       "\t(default 1).",        "I", 1, "-I <num>"));          result.addElement(new Option(       "\tmaximum number of iterations in the kMeans loop in\n" +       "\tthe Improve-Parameter part \n"+       "\t(default 1000).",        "M", 1, "-M <num>"));          result.addElement(new Option(       "\tmaximum number of iterations in the kMeans loop\n" +       "\tfor the splitted centroids in the Improve-Structure part \n"+       "\t(default 1000).",       "J", 1, "-J <num>"));          result.addElement(new Option(       "\tminimum number of clusters\n" +       "\t(default 2).",        "L", 1, "-L <num>"));          result.addElement(new Option(       "\tmaximum number of clusters\n" +       "\t(default 4).",       "H", 1, "-H <num>"));          result.addElement(new Option(       "\tdistance value for binary attributes\n" +       "\t(default 1.0).",       "V", 1, "-V <value>"));          result.addElement(new Option(       "\tFull class name of KDTree class to use, followed\n" +       "\tby scheme options.\n" +       "\teg: \"weka.core.KDTree -P\"\n" +       "\t(default no KDTree class used).",       "K", 1, "-K <KDTree class specification>"));          result.addElement(new Option(       "\tcutoff factor, takes the given percentage of the splitted \n" +       "\tcentroids if none of the children win\n" +       "\t(default 0.0).",       "C", 1, "-C <value>"));          result.addElement(new Option(       "\tFull class name of Distance function class to use, followed\n" +       "\tby scheme options.\n" +       "\teg: \"weka.core.MahalanobisDistance\"\n" +       "\t(default weka.core.EuclideanDistance).",       "D", 1, "-D <distance function class specification>"));          result.addElement(new Option(       "\tfile to read starting centers from (ARFF format).",       "N", 1, "-N <file name>"));          result.addElement(new Option(       "\tfile to write centers to (ARFF format).",       "O", 1, "-O <file name>"));     Enumeration en = super.listOptions();     while (en.hasMoreElements())       result.addElement(en.nextElement());          return result.elements();  }