Practical Data Mining

Monte F. Hancock, Jr., BA, MS, is Chief Scientist for Celestech, Inc., which has offices in Falls Church, Virginia, and Phoenix, Arizona. He was also a Technical Fellow at Northrop Grumman; Chief Cognitive Research Scientist for CSI, Inc., and was a software architect and engineer at Harris corporation, and HRB Singer, Inc. He has over 30 years of industry experience in software engineering and data mining technology development. He is also Adjunct Full Professor of Computer Science for the Webster University Space Coast Region, where he serves as Program Mentor for the Master of Science Degree in Computer Science. Monte has served for 26 years on the adjunct faculty in the Mathematics and Computer Science Department of the Hamilton Holt School of Rollins College, Winter Park, Florida, and served 3 semesters as adjunct Instructor in Computer Science at Pennsylvania State University. Monte teaches secondary Mathematics, AP Physics, Chemistry, Logic, Western Philosophy, and Church History at New Covenant School, and New Testament Greek at Heritage Christian Academy, both in Melbourne, Florida. He was a mathematics curriculum developer for the Department of Continuing Education of the University of Florida in Gainesville, and serves on the Industry Advisory Panels in Computer Science for both the Florida Institute of Technology, and Brevard Community College in Melbourne, Florida. Monte has twice served on panels for the National Science Foundation. Monte has served on many program committees for international data mining conferences, was a Session Chair for KDD. He has presented 15 conference papers, edited several book chapters, and co-authored the book Data Mining Explained with Rhonda Delmater, Digital Press, 2001. Monte is cited in (among others): "Who’s Who in the World" (2009–2012) "Who’s Who in America" (2009–2012) "Who’s Who in Science and Engineering" (2006–2012)

What Is Data Mining and What Can It Do? The Data Mining Process. Problem Definition (Step 1). Data Evaluation (Step 2). Feature Extraction and Enhancement (Step 3). Prototyping Plan and Model Development (Step 4). Model Evaluation (Step 5). Implementation (Step 6). Supervised Learning Genre Section 1—Detecting and Characterizing Known Patterns. Forensic Analysis Genre Section 2—Detecting, Characterizing, and Exploiting Hidden Patterns. Genre Section 3—Knowledge: Its Acquisition, Representation, and Use.