How to remove near duplicates e.g egg and eggs

KhibaKhiba Member Posts: 4 Newbie
I am a newbie and I apologize if the question is trivial.

I want to know how to find near duplicate entries in one attribute. For instance I want to treat tomato soup and tomatoe soup as a duplicate, egg and eggs as a duplicate. In your solution, kindly add a screenshot of the operators that you recommend using. 

Please help 

Best Answers

  • Options
    rfuentealbarfuentealba Moderator, RapidMiner Certified Analyst, Member, University Professor Posts: 568 Unicorn
    Solution Accepted
    Hello @Khiba,

    I don't know how your data looks like, but here is a process that does what you want. You need the Text Processing extension to run this process (and probably the Operator Toolbox if you run RapidMiner 9.1 or earlier).

    <?xml version="1.0" encoding="UTF-8"?><process version="9.2.000">
      <operator activated="true" class="process" compatibility="9.2.000" 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="utility:create_exampleset" compatibility="9.2.000" expanded="true" height="68" name="Create ExampleSet" width="90" x="45" y="34">
            <parameter key="generator_type" value="comma separated text"/>
            <parameter key="number_of_examples" value="100"/>
            <parameter key="use_stepsize" value="false"/>
            <list key="function_descriptions"/>
            <parameter key="add_id_attribute" value="false"/>
            <list key="numeric_series_configuration"/>
            <list key="date_series_configuration"/>
            <list key="date_series_configuration (interval)"/>
            <parameter key="date_format" value="yyyy-MM-dd HH:mm:ss"/>
            <parameter key="time_zone" value="SYSTEM"/>
            <parameter key="input_csv_text" value="id,text&#10;1,tomato tomatoes egg eggs soup salad&#10;2,potato potatoes person people sausage sausages"/>
            <parameter key="column_separator" value=","/>
            <parameter key="parse_all_as_nominal" value="false"/>
            <parameter key="decimal_point_character" value="."/>
            <parameter key="trim_attribute_names" value="true"/>
          <operator activated="true" class="set_role" compatibility="9.2.000" expanded="true" height="82" name="Set Role" width="90" x="179" y="34">
            <parameter key="attribute_name" value="id"/>
            <parameter key="target_role" value="id"/>
            <list key="set_additional_roles"/>
          <operator activated="true" class="nominal_to_text" compatibility="9.2.000" expanded="true" height="82" name="Nominal to Text" width="90" x="313" y="34">
            <parameter key="attribute_filter_type" value="all"/>
            <parameter key="attribute" value=""/>
            <parameter key="attributes" value=""/>
            <parameter key="use_except_expression" value="false"/>
            <parameter key="value_type" value="nominal"/>
            <parameter key="use_value_type_exception" value="false"/>
            <parameter key="except_value_type" value="file_path"/>
            <parameter key="block_type" value="single_value"/>
            <parameter key="use_block_type_exception" value="false"/>
            <parameter key="except_block_type" value="single_value"/>
            <parameter key="invert_selection" value="false"/>
            <parameter key="include_special_attributes" value="false"/>
          <operator activated="true" class="text:data_to_documents" compatibility="8.1.000" expanded="true" height="68" name="Data to Documents" width="90" x="447" y="34">
            <parameter key="select_attributes_and_weights" value="false"/>
            <list key="specify_weights"/>
          <operator activated="true" class="text:process_documents" compatibility="8.1.000" expanded="true" height="103" name="Process Documents" width="90" x="581" y="34">
            <parameter key="create_word_vector" value="true"/>
            <parameter key="vector_creation" value="TF-IDF"/>
            <parameter key="add_meta_information" value="true"/>
            <parameter key="keep_text" value="true"/>
            <parameter key="prune_method" value="none"/>
            <parameter key="prune_below_percent" value="3.0"/>
            <parameter key="prune_above_percent" value="30.0"/>
            <parameter key="prune_below_rank" value="0.05"/>
            <parameter key="prune_above_rank" value="0.95"/>
            <parameter key="datamanagement" value="double_sparse_array"/>
            <parameter key="data_management" value="auto"/>
            <process expanded="true">
              <operator activated="true" class="text:tokenize" compatibility="8.1.000" expanded="true" height="68" name="Tokenize" width="90" x="45" y="34">
                <parameter key="mode" value="non letters"/>
                <parameter key="characters" value=".:"/>
                <parameter key="language" value="English"/>
                <parameter key="max_token_length" value="3"/>
              <operator activated="true" class="text:stem_snowball" compatibility="8.1.000" expanded="true" height="68" name="Stem (Snowball)" width="90" x="179" y="34">
                <parameter key="language" value="English"/>
              <connect from_port="document" to_op="Tokenize" to_port="document"/>
              <connect from_op="Tokenize" from_port="document" to_op="Stem (Snowball)" to_port="document"/>
              <connect from_op="Stem (Snowball)" 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"/>
          <connect from_op="Create ExampleSet" from_port="output" to_op="Set Role" to_port="example set input"/>
          <connect from_op="Set Role" from_port="example set output" to_op="Nominal to Text" to_port="example set input"/>
          <connect from_op="Nominal to Text" from_port="example set output" to_op="Data to Documents" to_port="example set"/>
          <connect from_op="Data to Documents" from_port="documents" to_op="Process Documents" to_port="documents 1"/>
          <connect from_op="Process Documents" from_port="example set" to_port="result 1"/>
          <connect from_op="Process Documents" from_port="word list" 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"/>

    Hope this helps,

  • Options
    rfuentealbarfuentealba Moderator, RapidMiner Certified Analyst, Member, University Professor Posts: 568 Unicorn
    Solution Accepted
    Forgot to explain the process. I apologise, I'm a bit sick.

    What I did was:
    • Convert the data to documents (as required for text processing)
    • Process each document, and inside:
    • Tokenize (convert text into tokens)
    • Stem (I used the Snowball stemming algorithm).
    • The result you want can be a wordlist that you can convert it to data again or do what you need. It also comes as an example.
    Hope this helps.

    All the best,



  • Options
    KhibaKhiba Member Posts: 4 Newbie
    edited February 2019
    Thank you! And I hope that you feel better soon
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