Interactive Data Mining and Business Applications Rayid Ghani Collaboration with Chad Cumby, Divna Djordjevic, Andy Fano, Marko Krema, Mohit Kumar, Abhimanyu Lad, Yiming Yang

Accenture Technology LabsRayid Ghani Tradeoffs Cost (Time of human expert) Exploration (Future classifier performance) Exploitation (Relevancy to the expert) Exploration-Exploitation Tradeoffs Cost-Sensitive Active Learning Standard Ranking / Relevance Feedback Active Learning

Accenture Technology LabsRayid Ghani Case Studies Product Attribute Discovery & Extraction Health Insurance: Error Detection in Claims Social Media: Sentiment Analysis Knowledge Management: Form Filling

Accenture Technology LabsRayid Ghani Tradeoffs in Interactive Data Mining Cost (Time of human expert) Exploration (Future classifier performance) Exploitation (Relevancy to the expert)

Accenture Technology LabsRayid Ghani System Demo 4

Accenture Technology LabsRayid Ghani More Like This strategy Labeled Data Ranked List scored by classifier Select Top m% claims Rank Online Strategy Cluster

Accenture Technology LabsRayid Ghani Live System Results 90% relative improvement in accuracy over standard system 27% reduction in audit time ~$10 Million savings/year for a typical insurance company

Accenture Technology LabsRayid Ghani Summary Interactive Data Mining settings are prevalent in many business applications Challenge: efficiently calculate the incremental cost and benefit of any information that passes between expert and data mining system Allows users to control and manage tradeoffs making adoption easier and faster