Extract and save the items of each cluster in an Excel file

ahootanha

Hello
I kmeans clustered
I want to save the items of each cluster in an Excel file, but I do not know how to do it.
And
Is there a possibility of fuzzy clustering in the program?
Thanks

lionelderkrikor

Hi @ahootanha,

 

To answer to your first question, one method is to use the Filter Examples and Write Excel operators.

Here an example of process with 3 clusters : 

<?xml version="1.0" encoding="UTF-8"?><process version="8.1.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="8.1.000" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="8.1.000" expanded="true" height="68" name="Retrieve Iris" width="90" x="112" y="34">
        <parameter key="repository_entry" value="//Samples/data/Iris"/>
      </operator>
      <operator activated="true" class="concurrency:k_means" compatibility="8.1.000" expanded="true" height="82" name="Clustering" width="90" x="313" y="34">
        <parameter key="k" value="3"/>
      </operator>
      <operator activated="true" class="filter_examples" compatibility="8.1.000" expanded="true" height="103" name="Filter Examples" width="90" x="514" y="34">
        <list key="filters_list">
          <parameter key="filters_entry_key" value="cluster.equals.cluster_0"/>
        </list>
      </operator>
      <operator activated="true" class="write_excel" compatibility="8.1.000" expanded="true" height="82" name="Write Excel" width="90" x="715" y="34">
        <parameter key="excel_file" value="C:\Users\Lionel\Documents\Formations_DataScience\Rapidminer\Tests_Rapidminer\Extract_cluster\cluster_0.xlsx"/>
      </operator>
      <operator activated="true" class="filter_examples" compatibility="8.1.000" expanded="true" height="103" name="Filter Examples (2)" width="90" x="514" y="187">
        <list key="filters_list">
          <parameter key="filters_entry_key" value="cluster.equals.cluster_1"/>
        </list>
      </operator>
      <operator activated="true" class="filter_examples" compatibility="8.1.000" expanded="true" height="103" name="Filter Examples (3)" width="90" x="514" y="340">
        <list key="filters_list">
          <parameter key="filters_entry_key" value="cluster.equals.cluster_2"/>
        </list>
      </operator>
      <operator activated="true" class="write_excel" compatibility="8.1.000" expanded="true" height="82" name="Write Excel (2)" width="90" x="715" y="187">
        <parameter key="excel_file" value="C:\Users\Lionel\Documents\Formations_DataScience\Rapidminer\Tests_Rapidminer\Extract_cluster\cluster_1.xlsx"/>
      </operator>
      <operator activated="true" class="write_excel" compatibility="8.1.000" expanded="true" height="82" name="Write Excel (3)" width="90" x="715" y="340">
        <parameter key="excel_file" value="C:\Users\Lionel\Documents\Formations_DataScience\Rapidminer\Tests_Rapidminer\Extract_cluster\cluster_2.xlsx"/>
      </operator>
      <connect from_op="Retrieve Iris" from_port="output" to_op="Clustering" to_port="example set"/>
      <connect from_op="Clustering" from_port="clustered set" to_op="Filter Examples" to_port="example set input"/>
      <connect from_op="Filter Examples" from_port="example set output" to_op="Write Excel" to_port="input"/>
      <connect from_op="Filter Examples" from_port="original" to_op="Filter Examples (2)" to_port="example set input"/>
      <connect from_op="Write Excel" from_port="through" to_port="result 1"/>
      <connect from_op="Filter Examples (2)" from_port="example set output" to_op="Write Excel (2)" to_port="input"/>
      <connect from_op="Filter Examples (2)" from_port="original" to_op="Filter Examples (3)" to_port="example set input"/>
      <connect from_op="Filter Examples (3)" from_port="example set output" to_op="Write Excel (3)" to_port="input"/>
      <connect from_op="Write Excel (2)" from_port="through" to_port="result 2"/>
      <connect from_op="Write Excel (3)" from_port="through" to_port="result 3"/>
      <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"/>
      <portSpacing port="sink_result 4" spacing="0"/>
    </process>
  </operator>
</process>

To answer to your second question, I don't know what is "fuzzy clustering", but

if you don't know, a priori, the number of cluster(s), you can use the X-Means operator : This model determine

the correct number of cluster(s) based on the Bayesian Information Criteria (BIC).

 

I hope it helps,

 

Regards,

 

Lionel