Fake Data

This is simply available for visualization practice b/c it represents some interesting challenges. The clusterID in the last column of the line_counts_clusterID.csv aligns with the results; however, the results do not contain all of the same keys.

Huge thanks to @zanstrong for the drawings and brainstorming! Also, thanks to @s1nelson, @micahstubbs, @shirleyxywu and @enjalot for all of the thinking with me!

Fake Data Structure

1. line_counts_clusterID.csv - the last column is the cluster label. The preceeding 14 columns are boolean values that represent the presences of some features.
   
2. cluster#_results - additional information for each cluster that provides details on the following:
   • Gender
   • Language
   • Interests
   • TV Genre
   • TV Show
   • Location: Country
   • Location: Region
   • Location: Metro
   • Device Category
   • Device Wireless Network

Fake Data Visualization & Analysis

What is possible?

Analysis:
1. Multinomial Logistic Regression (thanks @s1nelson) 2. t-SNE (thanks @micahstubbs)

Visualization:
1. % Presence of each feature per each label.

• Heatmap (thanks @zanstrong)
  • click merge on feature selection
  • option to remove feature
  • separation between heatmap rows to indication disctions
  • How do the users arrive at the label?
• Node path ()
  • Similar to a decision tree to showcase paths by which tweets receive a label.
• Parallel Coordinates (thanks @shirleyxywu and @enjalot)
  • Turn on/off labels
  • Forground/Background colors instead of 14 different colors
  • Opacity for selected labels
  • randomly arround the feature ordering.
• Crossfilter (thanks @enjalot).
• The @zanstrong technique:

1. Difference from the mean count of each item per each cluster.