Descriptive Data Mining

David L. Olson is the James & H.K. Stuart Chancellor’s Distinguished Chair and Full Professor at the University of Nebraska.  He has published research in over 150 refereed journal articles, primarily on the topic of multiple objective decision-making, information technology, supply chain risk management, and data mining.  He teaches in the management information systems, management science, and operations management areas.  He has authored over 20 books. He is a member of the Decision Sciences Institute, the Institute for Operations Research and Management Sciences, and the Multiple Criteria Decision Making Society.  He was a Lowry Mays endowed Professor at Texas A&M University from 1999 to 2001.  He was named the Raymond E. Miles Distinguished Scholar award for 2002, and was a James C. and Rhonda Seacrest Fellow from 2005 to 2006.  He was named Best Enterprise Information Systems Educator by IFIP in 2006.  He is a Fellow of the DecisionSciences Institute.Georg Lauhoff is a Technologist at Western Digital Corporation and carries out R&D in materials science and its application in data storage devices and uses the techniques described in this book for his work. He co-authored 38 refereed journal articles and over 30 conference presentations, primarily on the topic of materials science, data storage materials and magnetic thin films. He was awarded scholarships and research grants in the U.K. and Japan. He was the Clerk Maxwell Scholar from 1995– 98 and is a Fellow of the Cambridge Philosophical Society. He studied physics at Aachen (Diplom) and Cambridge University (Master and Ph.D.) specializing in the field of materials science and magnetic thin films and sensors. After graduating he moved to Japan and held a faculty position in Materials Science and Engineering at the Toyota Technological Institute and, then carried out research in the sequencing of DNA using magnetic sensors at Cambridge University before moving in 2005 to the recording industry in the Bay area.

“The book is direct and easy to read, explaining how methods work without in-depth scholarly references. … this is a suitable book for data mining newcomers who are not interested in a theoretical understanding of the algorithms. … the book could be used as a course resource.” (Evangelia Kavakli, Computing Reviews, November 2, 2020)