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How to evaluate the best algorithm preform clusters?

halaalrobassyhalaalrobassy Member Posts: 16 Contributor II
edited June 2019 in Help
i have dataset and i want to cluster one feature to three clusters, i choose kmeans, kmedoid and xmean algorithms to preform this clustering then i want to evaluate which algorithm will perform better clustering. 
i put the three algorithms in loop parameter but i couldn't know where can i put the cluster distance performance operator  . i want to see the avg centroid and Davis bouldin measures for each model and according to them then choose the bset model will perform the best clustering . 

<?xml version="1.0" encoding="UTF-8"?><process version="9.2.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="6.0.002" expanded="true" name="Process">
    <parameter key="logverbosity" value="init"/>
    <parameter key="random_seed" value="2001"/>
    <parameter key="send_mail" value="never"/>
    <parameter key="notification_email" value=""/>
    <parameter key="process_duration_for_mail" value="30"/>
    <parameter key="encoding" value="SYSTEM"/>
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="9.2.001" expanded="true" height="68" name="Retrieve Engineering_majors1" width="90" x="45" y="34">
        <parameter key="repository_entry" value="../data/Engineering_majors1"/>
      </operator>
      <operator activated="true" class="select_attributes" compatibility="9.2.001" expanded="true" height="82" name="Select Attributes" width="90" x="179" y="85">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="c_cons_sum"/>
        <parameter key="attributes" value=""/>
        <parameter key="use_except_expression" value="false"/>
        <parameter key="value_type" value="attribute_value"/>
        <parameter key="use_value_type_exception" value="false"/>
        <parameter key="except_value_type" value="time"/>
        <parameter key="block_type" value="attribute_block"/>
        <parameter key="use_block_type_exception" value="false"/>
        <parameter key="except_block_type" value="value_matrix_row_start"/>
        <parameter key="invert_selection" value="false"/>
        <parameter key="include_special_attributes" value="false"/>
      </operator>
      <operator activated="true" class="normalize" compatibility="7.5.003" expanded="true" height="103" name="Normalize" width="90" x="313" y="136">
        <parameter key="return_preprocessing_model" value="false"/>
        <parameter key="create_view" value="true"/>
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="c_cons_sum"/>
        <parameter key="attributes" value=""/>
        <parameter key="use_except_expression" value="false"/>
        <parameter key="value_type" value="numeric"/>
        <parameter key="use_value_type_exception" value="false"/>
        <parameter key="except_value_type" value="real"/>
        <parameter key="block_type" value="value_series"/>
        <parameter key="use_block_type_exception" value="false"/>
        <parameter key="except_block_type" value="value_series_end"/>
        <parameter key="invert_selection" value="false"/>
        <parameter key="include_special_attributes" value="false"/>
        <parameter key="method" value="range transformation"/>
        <parameter key="min" value="0.0"/>
        <parameter key="max" value="1.0"/>
        <parameter key="allow_negative_values" value="false"/>
      </operator>
      <operator activated="true" class="loop_parameters" compatibility="6.0.003" expanded="true" height="103" name="Loop Parameters" width="90" x="447" y="136">
        <list key="parameters">
          <parameter key="Select Subprocess.select_which" value="[1.0;4;4;linear]"/>
        </list>
        <parameter key="error_handling" value="fail on error"/>
        <parameter key="synchronize" value="false"/>
        <process expanded="true">
          <operator activated="true" class="multiply" compatibility="9.2.001" expanded="true" height="82" name="Multiply" width="90" x="45" y="85"/>
          <operator activated="true" class="select_subprocess" compatibility="9.2.001" expanded="true" height="103" name="Select Subprocess" width="90" x="246" y="85">
            <parameter key="select_which" value="4"/>
            <process expanded="true">
              <operator activated="true" class="concurrency:k_means" compatibility="9.2.001" expanded="true" height="82" name="Clustering" width="90" x="45" y="85">
                <parameter key="add_cluster_attribute" value="true"/>
                <parameter key="add_as_label" value="true"/>
                <parameter key="remove_unlabeled" value="false"/>
                <parameter key="k" value="5"/>
                <parameter key="max_runs" value="10"/>
                <parameter key="determine_good_start_values" value="true"/>
                <parameter key="measure_types" value="BregmanDivergences"/>
                <parameter key="mixed_measure" value="MixedEuclideanDistance"/>
                <parameter key="nominal_measure" value="NominalDistance"/>
                <parameter key="numerical_measure" value="EuclideanDistance"/>
                <parameter key="divergence" value="SquaredEuclideanDistance"/>
                <parameter key="kernel_type" value="radial"/>
                <parameter key="kernel_gamma" value="1.0"/>
                <parameter key="kernel_sigma1" value="1.0"/>
                <parameter key="kernel_sigma2" value="0.0"/>
                <parameter key="kernel_sigma3" value="2.0"/>
                <parameter key="kernel_degree" value="3.0"/>
                <parameter key="kernel_shift" value="1.0"/>
                <parameter key="kernel_a" value="1.0"/>
                <parameter key="kernel_b" value="0.0"/>
                <parameter key="max_optimization_steps" value="100"/>
                <parameter key="use_local_random_seed" value="false"/>
                <parameter key="local_random_seed" value="1992"/>
              </operator>
              <connect from_port="input 1" to_op="Clustering" to_port="example set"/>
              <connect from_op="Clustering" from_port="cluster model" to_port="output 1"/>
              <connect from_op="Clustering" from_port="clustered set" to_port="output 2"/>
              <portSpacing port="source_input 1" spacing="0"/>
              <portSpacing port="source_input 2" spacing="0"/>
              <portSpacing port="sink_output 1" spacing="0"/>
              <portSpacing port="sink_output 2" spacing="0"/>
              <portSpacing port="sink_output 3" spacing="0"/>
            </process>
            <process expanded="true">
              <operator activated="true" class="k_medoids" compatibility="7.5.003" expanded="true" height="82" name="K-Medoids" width="90" x="45" y="187">
                <parameter key="add_cluster_attribute" value="true"/>
                <parameter key="add_as_label" value="true"/>
                <parameter key="remove_unlabeled" value="false"/>
                <parameter key="k" value="3"/>
                <parameter key="max_runs" value="10"/>
                <parameter key="max_optimization_steps" value="100"/>
                <parameter key="use_local_random_seed" value="true"/>
                <parameter key="local_random_seed" value="1992"/>
                <parameter key="measure_types" value="MixedMeasures"/>
                <parameter key="mixed_measure" value="MixedEuclideanDistance"/>
                <parameter key="nominal_measure" value="NominalDistance"/>
                <parameter key="numerical_measure" value="EuclideanDistance"/>
                <parameter key="divergence" value="GeneralizedIDivergence"/>
                <parameter key="kernel_type" value="radial"/>
                <parameter key="kernel_gamma" value="1.0"/>
                <parameter key="kernel_sigma1" value="1.0"/>
                <parameter key="kernel_sigma2" value="0.0"/>
                <parameter key="kernel_sigma3" value="2.0"/>
                <parameter key="kernel_degree" value="3.0"/>
                <parameter key="kernel_shift" value="1.0"/>
                <parameter key="kernel_a" value="1.0"/>
                <parameter key="kernel_b" value="0.0"/>
              </operator>
              <connect from_port="input 1" to_op="K-Medoids" to_port="example set"/>
              <connect from_op="K-Medoids" from_port="cluster model" to_port="output 1"/>
              <connect from_op="K-Medoids" from_port="clustered set" to_port="output 2"/>
              <portSpacing port="source_input 1" spacing="0"/>
              <portSpacing port="source_input 2" spacing="0"/>
              <portSpacing port="sink_output 1" spacing="0"/>
              <portSpacing port="sink_output 2" spacing="0"/>
              <portSpacing port="sink_output 3" spacing="0"/>
            </process>
            <process expanded="true">
              <operator activated="true" class="x_means" compatibility="9.2.001" expanded="true" height="82" name="X-Means" width="90" x="45" y="34">
                <parameter key="add_cluster_attribute" value="true"/>
                <parameter key="add_as_label" value="true"/>
                <parameter key="remove_unlabeled" value="false"/>
                <parameter key="k_min" value="3"/>
                <parameter key="k_max" value="60"/>
                <parameter key="determine_good_start_values" value="true"/>
                <parameter key="measure_types" value="NumericalMeasures"/>
                <parameter key="mixed_measure" value="MixedEuclideanDistance"/>
                <parameter key="nominal_measure" value="NominalDistance"/>
                <parameter key="numerical_measure" value="EuclideanDistance"/>
                <parameter key="divergence" value="GeneralizedIDivergence"/>
                <parameter key="kernel_type" value="radial"/>
                <parameter key="kernel_gamma" value="1.0"/>
                <parameter key="kernel_sigma1" value="1.0"/>
                <parameter key="kernel_sigma2" value="0.0"/>
                <parameter key="kernel_sigma3" value="2.0"/>
                <parameter key="kernel_degree" value="3.0"/>
                <parameter key="kernel_shift" value="1.0"/>
                <parameter key="kernel_a" value="1.0"/>
                <parameter key="kernel_b" value="0.0"/>
                <parameter key="clustering_algorithm" value="KMeans"/>
                <parameter key="max_runs" value="10"/>
                <parameter key="max_optimization_steps" value="100"/>
                <parameter key="use_local_random_seed" value="false"/>
                <parameter key="local_random_seed" value="1992"/>
              </operator>
              <connect from_port="input 1" to_op="X-Means" to_port="example set"/>
              <connect from_op="X-Means" from_port="cluster model" to_port="output 1"/>
              <connect from_op="X-Means" from_port="clustered set" to_port="output 2"/>
              <portSpacing port="source_input 1" spacing="0"/>
              <portSpacing port="source_input 2" spacing="0"/>
              <portSpacing port="sink_output 1" spacing="0"/>
              <portSpacing port="sink_output 2" spacing="0"/>
              <portSpacing port="sink_output 3" spacing="0"/>
            </process>
          </operator>
          <operator activated="true" class="cluster_distance_performance" compatibility="9.2.001" expanded="true" height="103" name="Performance" width="90" x="380" y="85">
            <parameter key="main_criterion" value="Avg. within centroid distance"/>
            <parameter key="main_criterion_only" value="false"/>
            <parameter key="normalize" value="false"/>
            <parameter key="maximize" value="false"/>
          </operator>
          <connect from_port="input 1" to_op="Multiply" to_port="input"/>
          <connect from_op="Multiply" from_port="output 1" to_op="Select Subprocess" to_port="input 1"/>
          <connect from_op="Select Subprocess" from_port="output 1" to_op="Performance" to_port="cluster model"/>
          <connect from_op="Select Subprocess" from_port="output 2" to_op="Performance" to_port="example set"/>
          <connect from_op="Performance" from_port="performance" to_port="performance"/>
          <connect from_op="Performance" from_port="example set" to_port="result 1"/>
          <connect from_op="Performance" from_port="cluster model" to_port="result 2"/>
          <portSpacing port="source_input 1" spacing="0"/>
          <portSpacing port="source_input 2" spacing="0"/>
          <portSpacing port="sink_performance" spacing="0"/>
          <portSpacing port="sink_result 1" spacing="0"/>
          <portSpacing port="sink_result 2" spacing="0"/>
          <portSpacing port="sink_result 3" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Retrieve Engineering_majors1" from_port="output" to_op="Select Attributes" to_port="example set input"/>
      <connect from_op="Select Attributes" from_port="example set output" to_op="Normalize" to_port="example set input"/>
      <connect from_op="Normalize" from_port="example set output" to_op="Loop Parameters" to_port="input 1"/>
      <connect from_op="Loop Parameters" from_port="result 1" to_port="result 1"/>
      <connect from_op="Loop Parameters" from_port="result 2" to_port="result 2"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
      <portSpacing port="sink_result 3" spacing="0"/>
    </process>
  </operator>
</process>


Best Answers

Answers

  • halaalrobassyhalaalrobassy Member Posts: 16 Contributor II
    thanks alot @Telcontar120
    yes i have a pirior cluster number for column values.just i want to ask you what do you mean by doing some logging for the result. I also want to optimize the performance of the clustering, is it possible ,if yes can you tell me how , please .
    Mosyafa
  • halaalrobassyhalaalrobassy Member Posts: 16 Contributor II
    thank you, your advices really helped me
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