Unsupervised Learning: Segmentation
The Data Science unsupervised learning demonstration conducts segmentation using Consumer Expenditure data. It utilizes several files that together demonstrate the functionality of the Spectrum Technology Platform Data Science Solution in Spectrum 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 for each of the included dataflows
- Output folder—The required output 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.