Doug Scott

Aston University

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James Di Francesco

Herzberg Institute of Astrophysics

Allen S. Lee

Janelia Research Campus

Roberto Abraham

University of Toronto

Michele T. Bannister

University of Canterbury

Oliver B. Downs

Princeton University

Sarah Sadavoy

Queen's University

David Rosenbluth

Lockheed Martin (United States)

Patrick Cocagne

Scott D. Moore

Mental Illness Research, Education and Clinical Centers

Alan Simchick

Cooperative Institutions

Herzberg Institute of Astrophysics

National Research Council Canada

Max Planck Institute for Astronomy

Max Planck Society

University of Victoria

Center for Astrophysics Harvard & Smithsonian

University of British Columbia

National Academies of Sciences, Engineering, and Medicine

Saskatchewan Health

Western University

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Michèle Friedrich Paul Schertz Doug Scott Ryan Hoest Catherine Chappell

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Unsupervised ranking and characterization of differentiated clusters

Luca Cazzanti Courosh Mehanian Julie E. Penzotti Doug Scott Oliver B. Downs

We describe a framework for automatically identifying and visualizing the most differentiating attributes of each cluster in a clustered data set. A dissimilarity function measures the cluster-conditional distinguishing saliency of each attribute with respect to a reference realization of the same attribute. For each cluster, the N attributes that are most dissimilar are presented first to the human expert, along with the overall dissimilarity of the cluster. We discuss the computational benefits of the proposed framework, how it can be implemented with map-reduce, its application to the behavioral analysis of mobile phone users, and it broad applicability to diverse problem domains.

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Mobile phone

10.1109/isi.2013.6578834

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Business Boost, the Cavendish Enterprise productivity improvement trial; using leading management practices with young businesses with fewer than 20 employees to improve productivity.

AEA Randomized Controlled Trials (2019)

Doug Scott

Enterprise Management

10.1257/rct.4111

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