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Interactive Data Mining and Business Applications

Explore the tradeoffs and benefits of interactive data mining in various business applications. Learn about cost-sensitive active learning, ranking and relevance feedback, and case studies in product attribute discovery, error detection in claims, and sentiment analysis on social media. Discover how interactive data mining can lead to significant savings, improved accuracy, and reduced audit time for insurance companies.

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Interactive Data Mining and Business Applications

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  1. Interactive Data Mining and Business ApplicationsRayid GhaniCollaboration with Chad Cumby, Divna Djordjevic, Andy Fano, Marko Krema, Mohit Kumar, Abhimanyu Lad, Yiming Yang

  2. Tradeoffs Cost-Sensitive Active Learning Exploration-Exploitation Tradeoffs Standard Ranking / Relevance Feedback Active Learning

  3. Case Studies Product Attribute Discovery & Extraction Health Insurance: Error Detection in Claims Knowledge Management: Form Filling Social Media: Sentiment Analysis

  4. Tradeoffs in Interactive Data Mining

  5. System Demo

  6. More Like This strategy Select Top m% claims Cluster Labeled Data Rank Ranked List scored by classifier Online Strategy

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

  8. 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

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