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[Solved] Add clustering label to dataset

Hi everybody ,

I am doing clustering in RapidMiner , my original dataset doesn't have any attribute for cluster id , but I want the the clustering attribute to be added to my dataset,

How can I do that ? because after clustering , I have access to the model not the dataset ,

Thanks in advance

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Skirzynski

Hey,

for most of our clustering operator you should see two output ports which offer the clustering model and the clustered set, which is you input example set + the cluster id.

If you just have the clustering model use the "Apply Model" operator to apply the model on a dataset and generate your desired clustering attribute.

Best Regards
Marcin

aryan_hosseinza

Thanks for the reply ,

Another question is : When I extract cluster prototype , my label attribute vanishes , what is the reason ? how can I preserve it

Skirzynski

Do you mean the cluster attribute? In my case it is preserved. Please post a process for your problem so i can take a look.

aryan_hosseinza

No , I mean label attribute, actually , I have a label attribute before & after clustering module , but I lose it after extracting clustering prototypes,

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.2.008">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.2.008" expanded="true" name="Process">
    <process expanded="true" height="539" width="1957">
      <operator activated="true" class="retrieve" compatibility="5.2.008" expanded="true" height="60" name="Retrieve" width="90" x="112" y="75">
        <parameter key="repository_entry" value="//NewLocalRepository/temp_5000sampled_MI4"/>
      </operator>
      <operator activated="true" class="nominal_to_numerical" compatibility="5.2.008" expanded="true" height="94" name="Nominal to Numerical" width="90" x="246" y="75">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="sex"/>
        <list key="comparison_groups"/>
      </operator>
      <operator activated="true" class="normalize" compatibility="5.2.008" expanded="true" height="94" name="Normalize" width="90" x="380" y="75"/>
      <operator activated="false" class="select_attributes" compatibility="5.2.008" expanded="true" height="76" name="Select Attributes" width="90" x="715" y="300">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="event"/>
        <parameter key="invert_selection" value="true"/>
      </operator>
      <operator activated="true" class="multiply" compatibility="5.2.008" expanded="true" height="76" name="Multiply" width="90" x="514" y="75"/>
      <operator activated="false" class="filter_examples" compatibility="5.2.008" expanded="true" height="76" name="Filter Examples (2)" width="90" x="849" y="255">
        <parameter key="condition_class" value="attribute_value_filter"/>
        <parameter key="parameter_string" value="event=t"/>
      </operator>
      <operator activated="true" class="filter_examples" compatibility="5.2.008" expanded="true" height="76" name="Filter Examples" width="90" x="849" y="30">
        <parameter key="condition_class" value="attribute_value_filter"/>
        <parameter key="parameter_string" value="event=f"/>
      </operator>
      <operator activated="false" class="k_means" compatibility="5.2.008" expanded="true" height="76" name="Clustering" width="90" x="1050" y="165">
        <parameter key="k" value="572"/>
        <parameter key="measure_types" value="MixedMeasures"/>
      </operator>
      <operator activated="true" class="k_medoids" compatibility="5.2.008" expanded="true" height="76" name="Clustering (2)" width="90" x="1117" y="30">
        <parameter key="k" value="572"/>
      </operator>
      <operator activated="true" class="extract_prototypes" compatibility="5.2.008" expanded="true" height="76" name="Extract Cluster Prototypes" width="90" x="1318" y="30"/>
      <operator activated="false" class="select_attributes" compatibility="5.2.008" expanded="true" height="76" name="Select Attributes (2)" width="90" x="1050" y="300">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="cluster"/>
        <parameter key="invert_selection" value="true"/>
        <parameter key="include_special_attributes" value="true"/>
      </operator>
      <operator activated="false" class="union" compatibility="5.2.008" expanded="true" height="76" name="Union" width="90" x="1452" y="165"/>
      <operator activated="false" class="select_attributes" compatibility="5.2.008" expanded="true" height="76" name="Select Attributes (3)" width="90" x="1586" y="165">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="id"/>
        <parameter key="invert_selection" value="true"/>
        <parameter key="include_special_attributes" value="true"/>
      </operator>
      <operator activated="false" class="shuffle" compatibility="5.2.008" expanded="true" height="76" name="Shuffle" width="90" x="1720" y="165"/>
      <connect from_op="Retrieve" from_port="output" to_op="Nominal to Numerical" to_port="example set input"/>
      <connect from_op="Nominal to Numerical" from_port="example set output" to_op="Normalize" to_port="example set input"/>
      <connect from_op="Normalize" from_port="example set output" to_op="Multiply" to_port="input"/>
      <connect from_op="Multiply" from_port="output 1" to_op="Filter Examples" to_port="example set input"/>
      <connect from_op="Filter Examples" from_port="example set output" to_op="Clustering (2)" to_port="example set"/>
      <connect from_op="Clustering (2)" from_port="cluster model" to_op="Extract Cluster Prototypes" to_port="model"/>
      <connect from_op="Extract Cluster Prototypes" from_port="example set" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
    </process>
  </operator>
</process>

MariusHelf

When you extract the cluster prototypes you create a completely new example set - so why should it have a label?

If you want the clustered data (including the label, if it has been in the data before the clustering), just use the second output of the Clustering operator.

Best, Marius

aryan_hosseinza

I am doing down sampling by choosing medoids of clustering , so I need the label attribute , Is there any way to preserve the label attribute too ?

MariusHelf

Yes, you have two options:

1. Join the extracted prototypes with the original data, use all attributes as key attributes:

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.3.000" expanded="true" name="Process">
    <process expanded="true" height="391" width="748">
      <operator activated="true" class="generate_data" compatibility="5.3.000" expanded="true" height="60" name="Generate Data" width="90" x="45" y="75">
        <parameter key="target_function" value="random classification"/>
        <parameter key="number_of_attributes" value="3"/>
      </operator>
      <operator activated="true" class="k_medoids" compatibility="5.3.000" expanded="true" height="76" name="Clustering" width="90" x="246" y="75"/>
      <operator activated="true" class="extract_prototypes" compatibility="5.3.000" expanded="true" height="76" name="Extract Cluster Prototypes" width="90" x="447" y="30"/>
      <operator activated="true" class="join" compatibility="5.3.000" expanded="true" height="76" name="Join" width="90" x="581" y="120">
        <parameter key="use_id_attribute_as_key" value="false"/>
        <list key="key_attributes">
          <parameter key="att1" value="att1"/>
          <parameter key="att2" value="att2"/>
          <parameter key="att3" value="att3"/>
        </list>
      </operator>
      <connect from_op="Generate Data" from_port="output" to_op="Clustering" to_port="example set"/>
      <connect from_op="Clustering" from_port="cluster model" to_op="Extract Cluster Prototypes" to_port="model"/>
      <connect from_op="Clustering" from_port="clustered set" to_op="Join" to_port="right"/>
      <connect from_op="Extract Cluster Prototypes" from_port="example set" to_op="Join" to_port="left"/>
      <connect from_op="Join" from_port="join" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="90"/>
      <portSpacing port="sink_result 2" spacing="0"/>
    </process>
  </operator>
</process>

2. Set the role of the label to regular before clustering. Warning: that way the label will be considered for clustering. This is not always what you want.

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.3.000" expanded="true" name="Process">
    <process expanded="true" height="391" width="748">
      <operator activated="true" class="generate_data" compatibility="5.3.000" expanded="true" height="60" name="Generate Data" width="90" x="45" y="75">
        <parameter key="target_function" value="random classification"/>
        <parameter key="number_of_attributes" value="3"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="5.3.000" expanded="true" height="76" name="Set Role" width="90" x="179" y="75">
        <parameter key="name" value="label"/>
        <list key="set_additional_roles"/>
      </operator>
      <operator activated="true" class="k_medoids" compatibility="5.3.000" expanded="true" height="76" name="Clustering" width="90" x="313" y="75"/>
      <operator activated="true" class="extract_prototypes" compatibility="5.3.000" expanded="true" height="76" name="Extract Cluster Prototypes" width="90" x="447" y="75"/>
      <operator activated="true" class="set_role" compatibility="5.3.000" expanded="true" height="76" name="Set Role (2)" width="90" x="581" y="75">
        <parameter key="name" value="label"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles"/>
      </operator>
      <connect from_op="Generate Data" from_port="output" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Clustering" to_port="example set"/>
      <connect from_op="Clustering" from_port="cluster model" to_op="Extract Cluster Prototypes" to_port="model"/>
      <connect from_op="Extract Cluster Prototypes" from_port="example set" to_op="Set Role (2)" to_port="example set input"/>
      <connect from_op="Set Role (2)" from_port="example set output" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="36"/>
      <portSpacing port="sink_result 2" spacing="0"/>
    </process>
  </operator>
</process>

Happy Mining!
~Marius

aryan_hosseinza

Thanks for your help,

But there's a problem , the dataset is very large (550K of examples with more than 700 attributes) , so joining is not applicable I guess ,

Do you have any idea how I can down sample such a large dataset ? maybe another way not extracting prototypes

MariusHelf

You could mis-use k-NN with k=1 for it. Won't be exactly what you call "fast", but it will work:

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.3.000" expanded="true" name="Process">
    <process expanded="true" height="391" width="748">
      <operator activated="true" class="generate_data" compatibility="5.3.000" expanded="true" height="60" name="Generate Data" width="90" x="45" y="75">
        <parameter key="target_function" value="random classification"/>
        <parameter key="number_of_attributes" value="3"/>
      </operator>
      <operator activated="true" class="multiply" compatibility="5.3.000" expanded="true" height="94" name="Multiply" width="90" x="179" y="75"/>
      <operator activated="true" class="k_medoids" compatibility="5.3.000" expanded="true" height="76" name="Clustering" width="90" x="313" y="75"/>
      <operator activated="true" class="extract_prototypes" compatibility="5.3.000" expanded="true" height="76" name="Extract Cluster Prototypes" width="90" x="447" y="75"/>
      <operator activated="true" class="k_nn" compatibility="5.3.000" expanded="true" height="76" name="k-NN" width="90" x="313" y="210"/>
      <operator activated="true" class="apply_model" compatibility="5.3.000" expanded="true" height="76" name="Apply Model" width="90" x="581" y="165">
        <list key="application_parameters"/>
      </operator>
      <connect from_op="Generate Data" from_port="output" to_op="Multiply" to_port="input"/>
      <connect from_op="Multiply" from_port="output 1" to_op="Clustering" to_port="example set"/>
      <connect from_op="Multiply" from_port="output 2" to_op="k-NN" to_port="training set"/>
      <connect from_op="Clustering" from_port="cluster model" to_op="Extract Cluster Prototypes" to_port="model"/>
      <connect from_op="Extract Cluster Prototypes" from_port="example set" to_op="Apply Model" to_port="unlabelled data"/>
      <connect from_op="k-NN" from_port="model" to_op="Apply Model" to_port="model"/>
      <connect from_op="Apply Model" from_port="labelled data" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="36"/>
      <portSpacing port="sink_result 2" spacing="0"/>
    </process>
  </operator>
</process>

aryan_hosseinza

I used the following model , but there's a problem , after applying the cluster model , I want to invert-select cluster attribute (I want to remove it ) but there's no such attribute in the list , but when I get the result it shows me this attribute

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.2.008">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.2.008" expanded="true" name="Process">
    <process expanded="true" height="539" width="1957">
      <operator activated="true" class="retrieve" compatibility="5.2.008" expanded="true" height="60" name="Retrieve" width="90" x="45" y="75">
        <parameter key="repository_entry" value="//NewLocalRepository/temp_5000sampled_MI4"/>
      </operator>
      <operator activated="true" class="sample_stratified" compatibility="5.2.008" expanded="true" height="76" name="Sample (Stratified)" width="90" x="179" y="255">
        <parameter key="sample" value="relative"/>
      </operator>
      <operator activated="true" class="nominal_to_numerical" compatibility="5.2.008" expanded="true" height="94" name="Nominal to Numerical" width="90" x="246" y="75">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="sex"/>
        <list key="comparison_groups"/>
      </operator>
      <operator activated="true" class="normalize" compatibility="5.2.008" expanded="true" height="94" name="Normalize" width="90" x="380" y="75"/>
      <operator activated="false" class="select_attributes" compatibility="5.2.008" expanded="true" height="76" name="Select Attributes" width="90" x="179" y="345">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="event"/>
        <parameter key="invert_selection" value="true"/>
      </operator>
      <operator activated="true" class="multiply" compatibility="5.2.008" expanded="true" height="94" name="Multiply" width="90" x="581" y="75"/>
      <operator activated="true" class="filter_examples" compatibility="5.2.008" expanded="true" height="76" name="Filter Examples (2)" width="90" x="782" y="255">
        <parameter key="condition_class" value="attribute_value_filter"/>
        <parameter key="parameter_string" value="event=t"/>
      </operator>
      <operator activated="true" class="filter_examples" compatibility="5.2.008" expanded="true" height="76" name="Filter Examples" width="90" x="782" y="30">
        <parameter key="condition_class" value="attribute_value_filter"/>
        <parameter key="parameter_string" value="event=f"/>
      </operator>
      <operator activated="true" class="multiply" compatibility="5.2.008" expanded="true" height="94" name="Multiply (2)" width="90" x="916" y="30"/>
      <operator activated="true" class="k_means" compatibility="5.2.008" expanded="true" height="76" name="Clustering" width="90" x="1117" y="30">
        <parameter key="k" value="5"/>
        <parameter key="max_runs" value="100"/>
        <parameter key="measure_types" value="MixedMeasures"/>
      </operator>
      <operator activated="true" class="apply_model" compatibility="5.2.008" expanded="true" height="76" name="Apply Model" width="90" x="1117" y="120">
        <list key="application_parameters"/>
      </operator>
      <operator activated="true" class="union" compatibility="5.2.008" expanded="true" height="76" name="Union" width="90" x="1385" y="210"/>
      <operator activated="true" class="select_attributes" compatibility="5.2.008" expanded="true" height="76" name="Select Attributes (3)" width="90" x="1519" y="210">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="id"/>
        <parameter key="invert_selection" value="true"/>
        <parameter key="include_special_attributes" value="true"/>
      </operator>
      <operator activated="true" class="shuffle" compatibility="5.2.008" expanded="true" height="76" name="Shuffle" width="90" x="1720" y="210"/>
      <connect from_op="Retrieve" from_port="output" to_op="Sample (Stratified)" to_port="example set input"/>
      <connect from_op="Sample (Stratified)" from_port="example set output" to_op="Nominal to Numerical" to_port="example set input"/>
      <connect from_op="Nominal to Numerical" from_port="example set output" to_op="Normalize" to_port="example set input"/>
      <connect from_op="Normalize" from_port="example set output" to_op="Multiply" to_port="input"/>
      <connect from_op="Multiply" from_port="output 1" to_op="Filter Examples" to_port="example set input"/>
      <connect from_op="Multiply" from_port="output 2" to_op="Filter Examples (2)" to_port="example set input"/>
      <connect from_op="Filter Examples (2)" from_port="example set output" to_op="Union" to_port="example set 2"/>
      <connect from_op="Filter Examples" from_port="example set output" to_op="Multiply (2)" to_port="input"/>
      <connect from_op="Multiply (2)" from_port="output 1" to_op="Clustering" to_port="example set"/>
      <connect from_op="Multiply (2)" from_port="output 2" to_op="Apply Model" to_port="unlabelled data"/>
      <connect from_op="Clustering" from_port="cluster model" to_op="Apply Model" to_port="model"/>
      <connect from_op="Apply Model" from_port="labelled data" to_op="Union" to_port="example set 1"/>
      <connect from_op="Union" from_port="union" to_op="Select Attributes (3)" to_port="example set input"/>
      <connect from_op="Select Attributes (3)" from_port="example set output" to_op="Shuffle" to_port="example set input"/>
      <connect from_op="Shuffle" from_port="example set output" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
    </process>
  </operator>
</process>

MariusHelf

I did not look at the process, but if an attribute is not shown in the list, you can simply enter it manually, even though it's not in the drop-down-list.

aryan_hosseinza

it works fine now
I appreciate your help,
Thanks