'nominal correllation matrix' from example set

imkeimke Member Posts: 12 Contributor I
edited October 2019 in Help


I have already done a text mining process and now I have the Example Set (Process Documents from Data) table. With this I want to calculate how often two words occur in the same text. At first I thought I could use Correlation Matrix Operator, but that does not work. So I tryed with Auto Model the Clustring, but for this I can only take two entrys of the example Set and I want to know it from all the words. So I thought maybe I could add the x-Means Operator in my process, but for x-Means my Data Set is a way to big and with k-Means I'm not getting the results I want. (No Correlation Matrix like with Auto Model anymore).

So  my question is: Is there a possibility to create a correlation Matrix with the ExampleSet?

Thank you


Best Answer

  • Options
    MartinLiebigMartinLiebig Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,525 RM Data Scientist
    Solution Accepted

    Hi @imke,

    it feels to me like this is a case for FP-Growth or for n_grams? See attached example.





    <?xml version="1.0" encoding="UTF-8"?><process version="9.0.002">
    <operator activated="true" class="process" compatibility="9.0.002" expanded="true" name="Process">
    <process expanded="true">
    <operator activated="true" class="text:create_document" compatibility="8.1.000" expanded="true" height="68" name="Create Document" width="90" x="112" y="85">
    <parameter key="text" value="one two three"/>
    <operator activated="true" class="text:create_document" compatibility="8.1.000" expanded="true" height="68" name="Create Document (2)" width="90" x="112" y="238">
    <parameter key="text" value="two three four"/>
    <operator activated="true" class="collect" compatibility="9.0.002" expanded="true" height="103" name="Collect" width="90" x="246" y="187"/>
    <operator activated="true" class="text:process_documents" compatibility="8.1.000" expanded="true" height="103" name="Process Documents" width="90" x="380" y="187">
    <parameter key="vector_creation" value="Binary Term Occurrences"/>
    <process expanded="true">
    <operator activated="true" class="text:tokenize" compatibility="8.1.000" expanded="true" height="68" name="Tokenize" width="90" x="380" y="187"/>
    <connect from_port="document" to_op="Tokenize" to_port="document"/>
    <connect from_op="Tokenize" from_port="document" to_port="document 1"/>
    <portSpacing port="source_document" spacing="0"/>
    <portSpacing port="sink_document 1" spacing="0"/>
    <portSpacing port="sink_document 2" spacing="0"/>
    <description align="center" color="transparent" colored="false" width="126">Binary term occs</description>
    <operator activated="true" class="numerical_to_binominal" compatibility="9.0.002" expanded="true" height="82" name="Numerical to Binominal" width="90" x="581" y="187"/>
    <operator activated="true" class="concurrency:fp_growth" compatibility="9.0.002" expanded="true" height="82" name="FP-Growth" width="90" x="715" y="187">
    <enumeration key="must_contain_list"/>
    <connect from_op="Create Document" from_port="output" to_op="Collect" to_port="input 1"/>
    <connect from_op="Create Document (2)" from_port="output" to_op="Collect" to_port="input 2"/>
    <connect from_op="Collect" from_port="collection" to_op="Process Documents" to_port="documents 1"/>
    <connect from_op="Process Documents" from_port="example set" to_op="Numerical to Binominal" to_port="example set input"/>
    <connect from_op="Numerical to Binominal" from_port="example set output" to_op="FP-Growth" to_port="example set"/>
    <connect from_op="FP-Growth" from_port="frequent sets" 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"/>
    - Sr. Director Data Solutions, Altair RapidMiner -
    Dortmund, Germany


  • Options
    imkeimke Member Posts: 12 Contributor I

    Hello Martin,

    that's quite good, but not the right solution for me I think. N-grams are only words which are following themselfs and I want to know, which words are in wich text together, but not directly after the other word. Do you know what I mean?



  • Options
    imkeimke Member Posts: 12 Contributor I

    Hello Martin,

    I need to correct myself. With the right settings FP-Growth is perfect for me!

    Thanks a lot!


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