Unsupervised Learning: Segmentation
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.