Description
Hamparsum Bozdogan – Statistical Data Mining & Knowledge Discovery
Massive data sets pose a great challenge to many cross-disciplinary fields, including statistics. The high dimensionality and different data types and structures have now outstripped the capabilities of traditional statistical, graphical, and data visualization tools. Extracting useful information from such large data sets calls for novel approaches that meld concepts, tools, and techniques from diverse areas, such as computer science, statistics, artificial intelligence, and financial engineering.
Statistical Data Mining and Knowledge Discovery brings together a stellar panel of experts to discuss and disseminate recent developments in data analysis techniques for data mining and knowledge extraction. This carefully edited collection provides a practical, multidisciplinary perspective on using statistical techniques in areas such as market segmentation, customer profiling, image and speech analysis, and fraud detection. The chapter authors, who include such luminaries as Arnold Zellner, S. James Press, Stephen Fienberg, and Edward K. Wegman, present novel approaches and innovative models and relate their experiences in using data mining techniques in a wide range of applications.
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Bradley Cowan – Pentagonal Time Cycle Theory
Jeanne Long – Basic Astrotech
Jeffrey J.Reuner – Real Options Theory
Mohinder S. Grewal, Angus P. Andrews – Kalman filtering. Theory and Practice Using Matlab
Jan Brinkhuis, Vkadimir Tikhomirov – Optimization. Insights and Applications
Sean Carpenter – Government Funding Solutions Basic
Dr. Mircea Dologa – Theory & Practice. Integrated Pithfork Analysis
Ronald R.Hocking – Methods and Applications of Linear Models
Afshin Taghechian – DayTrading the S&P 500 & TS Code
Charles Morris – Money Greed and Risk
Vladimir Cherkassky, Filip Mulier – Learning from Data. Concepts, Theory & Methods
Brian J.Taylor – Methods & Procedures for the Verification & Validation of Artificial NN

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