• Algorithmic Reason: The New Government of Self and Other,  Claudia Aradau and Tobias Blanke, referenced on pages 32 and 56, Oxford University Press, 2022.
• Modern Analytics Methodologies, by Michele Chambers and Thomas Dinsmore, referenced on pages 26, 45-50, and 52.
• The Power of Habit, by Charles Duhigg, referenced on page 224, Chapter 7.
• Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, by Eric Siegel, referenced on pages 55, 143-144, and 148.
• Investigative Data Mining for Security and Criminal Detection, by Jesus Mena, referenced on pages 213 and 219.

• TechGuide Podcast Interview
• Berkeley Haas School of Business Interview
• Data Science with Harshit Interview
• KNIME Fireside Chat

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• Statistical Analysis & Data Mining Applications (Nisbet, Elder, Miner, Elsevier, 2009),
• Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications (Miner, Elder, Hill, Nisbet, Delen, and Fast, Academic Press, 2012), , Forward #3,
• Practical Data Analytics for Innovation in Medicine: Building Real Predictive and Prescriptive Models in Personalized Healthcare and Medical Research (Miner, Delen, Elder et al, Elsevier, 2012)
• Big Data, Data Mining, and Machine Learning (Dean, Wiley, 2014)

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• KDD 2010 Conference, Industrial Track Committee

• Third Workshop on Data Mining Case Studies and Practice Prize (2009); Organizing Committee

• KDD 2006 Conference, Industrial Track Committee

• KDD 2004 Conference, Industrial Track Committee

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1. Input Shuffling, 2016
   Developed new algorithm to assess variable significance in predictive models, regardless of the algorithm. Discovered later it is similar to “Permutation Importance” by Leo Breiman, but has some significant improvements. Ranks variables in a generally similar way to SHAP Values.
2. Classification Rules, 2010
   Turns association rules into a classifier. Was implemented in Predixion software. Included in Chapter 5 of Applied Predictive Analytics (Wiley 2014)
3. Effects of Normalization on Squared-Error algorithms (KNIME Summit, Austin TX, 2019)
   PCA, K-Means Clustering, kNN
4. Binary Classification on Target Variables with Unbalanced Distributions (Predictive Analytics World, 2013)
   Pushing back against the “consensus of the masses” on how to ensure classifiers work correctly and work well with unbalanced distributions for target variables. Spoken on this at multiple conferences, on my blog, and commenting 
5. Association Rules for Interaction Detection with classification modeling, 2012
   Uses association rules to find combinations of variables that make good interactions. published in Applied Predictive Analytics (Wiley, 2014). Work continuing.

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