"Can I label the data using the Apriori Algorithm?"

mario_sark

Hello All, 

My Question is in RapidMiner , once i created rules using Apriori algorithm , can add an attribute which tell me this customer on which rule belongs ? 

the objective is that I've created an RFM Analysis based on Transactions, the next step was that i clustered my customer using K-Means Based on RFM into 3 clusters. the next step will be to do a profiling for these clusters . i need to add an attribute which tells me this customer which profile he/her belongs. 

Thank you in Advance, 
Mario

yyhuang

Hi @mario_sark,

Maybe a decision tree that leverage the cluster labels from k-means would help understanding the rules for clustering. You can also add operator toolbox extension for the "get decision tree path" which will explain the segmentation rules for  each customer.

I used the ICU patient data as example.

<?xml version="1.0" encoding="UTF-8"?><process version="9.2.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <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="retrieve" compatibility="9.2.000" expanded="true" height="68" name="Retrieve ICU Morbidity (cour. Sven Van Poucke)" width="90" x="112" y="34">
        <parameter key="repository_entry" value="//Community Samples/Community Data Sets/Medical and Health/ICU Morbidity (cour. Sven Van Poucke)"/>
      </operator>
      <operator activated="true" class="numerical_to_polynominal" compatibility="9.2.000" expanded="true" height="82" name="Numerical to Polynominal" width="90" x="246" y="34">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="icustay_id"/>
        <parameter key="attributes" value=""/>
        <parameter key="use_except_expression" value="false"/>
        <parameter key="value_type" value="numeric"/>
        <parameter key="use_value_type_exception" value="false"/>
        <parameter key="except_value_type" value="real"/>
        <parameter key="block_type" value="value_series"/>
        <parameter key="use_block_type_exception" value="false"/>
        <parameter key="except_block_type" value="value_series_end"/>
        <parameter key="invert_selection" value="false"/>
        <parameter key="include_special_attributes" value="false"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="9.2.000" expanded="true" height="82" name="Set Role" width="90" x="380" y="34">
        <parameter key="attribute_name" value="icustay_id"/>
        <parameter key="target_role" value="id"/>
        <list key="set_additional_roles"/>
        <description align="center" color="transparent" colored="false" width="126">icustay_id is an unique identifier for the patients</description>
      </operator>
      <operator activated="true" class="replace_missing_values" compatibility="9.2.000" expanded="true" height="103" name="Replace Missing Values" width="90" x="581" y="34">
        <parameter key="return_preprocessing_model" value="false"/>
        <parameter key="create_view" value="false"/>
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="gender"/>
        <parameter key="attributes" value=""/>
        <parameter key="use_except_expression" value="false"/>
        <parameter key="value_type" value="attribute_value"/>
        <parameter key="use_value_type_exception" value="false"/>
        <parameter key="except_value_type" value="time"/>
        <parameter key="block_type" value="attribute_block"/>
        <parameter key="use_block_type_exception" value="false"/>
        <parameter key="except_block_type" value="value_matrix_row_start"/>
        <parameter key="invert_selection" value="false"/>
        <parameter key="include_special_attributes" value="false"/>
        <parameter key="default" value="value"/>
        <list key="columns"/>
        <parameter key="replenishment_value" value="UNK"/>
      </operator>
      <operator activated="true" class="concurrency:k_means" compatibility="9.2.000" expanded="true" height="82" name="Clustering" width="90" x="715" y="34">
        <parameter key="add_cluster_attribute" value="true"/>
        <parameter key="add_as_label" value="true"/>
        <parameter key="remove_unlabeled" value="false"/>
        <parameter key="k" value="5"/>
        <parameter key="max_runs" value="10"/>
        <parameter key="determine_good_start_values" value="true"/>
        <parameter key="measure_types" value="MixedMeasures"/>
        <parameter key="mixed_measure" value="MixedEuclideanDistance"/>
        <parameter key="nominal_measure" value="NominalDistance"/>
        <parameter key="numerical_measure" value="EuclideanDistance"/>
        <parameter key="divergence" value="SquaredEuclideanDistance"/>
        <parameter key="kernel_type" value="radial"/>
        <parameter key="kernel_gamma" value="1.0"/>
        <parameter key="kernel_sigma1" value="1.0"/>
        <parameter key="kernel_sigma2" value="0.0"/>
        <parameter key="kernel_sigma3" value="2.0"/>
        <parameter key="kernel_degree" value="3.0"/>
        <parameter key="kernel_shift" value="1.0"/>
        <parameter key="kernel_a" value="1.0"/>
        <parameter key="kernel_b" value="0.0"/>
        <parameter key="max_optimization_steps" value="100"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
      </operator>
      <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.2.000" expanded="true" height="103" name="Decision Tree" width="90" x="849" y="34">
        <parameter key="criterion" value="gain_ratio"/>
        <parameter key="maximal_depth" value="20"/>
        <parameter key="apply_pruning" value="true"/>
        <parameter key="confidence" value="0.1"/>
        <parameter key="apply_prepruning" value="true"/>
        <parameter key="minimal_gain" value="0.01"/>
        <parameter key="minimal_leaf_size" value="2"/>
        <parameter key="minimal_size_for_split" value="4"/>
        <parameter key="number_of_prepruning_alternatives" value="3"/>
      </operator>
      <operator activated="true" class="operator_toolbox:get_dectree_path" compatibility="1.8.000" expanded="true" height="82" name="Get Decision Tree Path" width="90" x="983" y="34"/>
      <connect from_op="Retrieve ICU Morbidity (cour. Sven Van Poucke)" from_port="output" to_op="Numerical to Polynominal" to_port="example set input"/>
      <connect from_op="Numerical to Polynominal" from_port="example set output" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Replace Missing Values" to_port="example set input"/>
      <connect from_op="Replace Missing Values" from_port="example set output" to_op="Clustering" to_port="example set"/>
      <connect from_op="Clustering" from_port="clustered set" to_op="Decision Tree" to_port="training set"/>
      <connect from_op="Decision Tree" from_port="model" to_op="Get Decision Tree Path" to_port="mod"/>
      <connect from_op="Decision Tree" from_port="exampleSet" to_op="Get Decision Tree Path" to_port="exa"/>
      <connect from_op="Get Decision Tree Path" from_port="exa" to_port="result 1"/>
      <connect from_op="Get Decision Tree Path" from_port="mod" 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"/>
    </process>
  </operator>
</process>

YY

mario_sark

yyhuang  you are right this what i want !!!!