How to load transaction (basket) data in RapidMiner for association rule?

I have comma separated transaction (basket) data in itemsets format

citrus fruit,semi-finished,bread,margarine
tropical fruit,yogurt,coffee,milk
yogurt,cream,cheese,meat spreads
 etc

where each row indicates the items purchased in a single transaction. By using Read.CSV operator i loaded this file in RapidMiner. I could not find any operator to transform this data for FP-growth and association rule mining.

Is there any way to read such type of file in RapidMiner for association rule mining?

I finally understood what you meant - sorry I was being slow. This can be done using operators from the Text Processing Extension. You have to install this from the RapidMiner repository. Once you have you can try this process.

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="7.0.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="7.0.000" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="read_csv" compatibility="7.0.000" expanded="true" height="68" name="Read CSV" width="90" x="246" y="85">
        <parameter key="csv_file" value="C:\Temp\is.txt"/>
        <parameter key="column_separators" value="\r\n"/>
        <parameter key="first_row_as_names" value="false"/>
        <list key="annotations"/>
        <parameter key="encoding" value="windows-1252"/>
        <list key="data_set_meta_data_information">
          <parameter key="0" value="att1.true.polynominal.attribute"/>
        </list>
      </operator>
      <operator activated="true" class="nominal_to_text" compatibility="7.0.000" expanded="true" height="82" name="Nominal to Text" width="90" x="380" y="85"/>
      <operator activated="true" class="text:process_document_from_data" compatibility="7.0.000" expanded="true" height="82" name="Process Documents from Data" width="90" x="514" y="85">
        <parameter key="vector_creation" value="Term Occurrences"/>
        <list key="specify_weights"/>
        <process expanded="true">
          <operator activated="true" class="text:tokenize" compatibility="7.0.000" expanded="true" height="68" name="Tokenize" width="90" x="45" y="34">
            <parameter key="mode" value="specify characters"/>
            <parameter key="characters" value=","/>
          </operator>
          <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"/>
        </process>
      </operator>
      <connect from_op="Read CSV" from_port="output" to_op="Nominal to Text" to_port="example set input"/>
      <connect from_op="Nominal to Text" from_port="example set output" to_op="Process Documents from Data" to_port="example set"/>
      <connect from_op="Process Documents from Data" 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>

The trick is to use Read CSV to read the original file in but use end of line as the delimiter. This reads the entire line in as a polynominal attribute. From there, you have to convert this to text so that the text processing operators can do their work. The Process Documents from Data operator is then used to make the final example set. The important point is to use the Tokenize operator to split the lines into words separated by commas.