Unsupervised Learning: Segmentation

Download the unsupervised learning demonstration

The Data Science unsupervised learning demonstration conducts segementation using Consumer Expenditure data. It utilizes several files that together demonstrate the functionality of the Spectrum™ Technology Platform Data Science Solution in Enterprise Designer.

Spectrum_DataScience_Unsupervised_Learning.zip includes the following files:
  • Spectrum_DataScience_Unsupervised_Learning.pdf—Documentation that walks you through how to build and use the primary dataflow, the subflow, the scoring dataflow, and all supporting files
  • Data.zip—The required input files and output files for each of the included dataflows
    • Input folder—The required input files files for each of the included dataflows
    • Output folder—The required output files files for each of the included dataflows
    • PythonBased folder—Required input and output files to use optional Python processing in lieu of Group Statistics and Transformer stages in primary dataflow
  • Consumer_Expenditure_Demo_DF_(v12.1).zip—The dataflows for Spectrum™ Technology Platform 12.1
    • ConsumerExpenditure_v121_sampleandcluster.df
    • ConsumerExpenditure_v121_sampleandcluster_subflow.df
    • ConsumerExpenditure_v121_score.df
    • ConsumerExpenditure_v121_subflow.df
    • PythonBased folder—Required dataflows, process flows, bat script, Python script and documentation to use optional Python processing in lieu of Group Statistics and Transformer stages in primary dataflow
  • Consumer_Expenditure_Demo_DF_(v12.2).zip—The dataflows for Spectrum™ Technology Platform 12.2
    • ConsumerExpenditure_v122_sampleandcluster.df
    • ConsumerExpenditure_v122_sampleandcluster_subflow.df
    • ConsumerExpenditure_v122_score.df
    • ConsumerExpenditure_v122_subflow.df
    • PythonBased folder—Required dataflows, process flows, bat script, Python script and documentation to use optional Python processing in lieu of Group Statistics and Transformer stages in primary dataflow
  • ReadMe.txt—High-level descriptions and instructions for the previously mentioned files.
You can create your own dataflow by following the step-by-step instructions in the documentation, or you can use the included dataflows as references to confirm what the individual completed stages and dataflows as a whole should look like.