Data Mining: Foundations and Intelligent Paradigms Volume 2: Statistical, Bayesian, Time Series and other Theoretical Aspects - Holmes, Dawn E.; Jain, Lakhmi C.; SpringerLink (Online service) (Berlin, Heidelberg : Springer Berlin Heidelberg, 2012)
Data mining is one of the most rapidly growing research areas in computer science and statistics. In Volume 2 of this three volume series, we have brought together contributions from some of the most prestigious researchers in theoretical data mining. Each of the chapters is self contained. Statisticians and applied scientists/ engineers will find this volume valuable. Additionally, it provides a sourcebook for graduate students interested in the current direction of research in data mining.
From the content: Data Mining with Multilayer Perceptrons and Support Vector Machines -- Regulatory Networks under Ellipsoidal Uncertainty - Data Analysis and Prediction by Optimization Theory and Dynamical Systems -- A Visual Environment for Designing and Running Data Mining Workflows in the Knowledge Grid -- Formal framework for the Study of Algorithmic Properties of Objective Interestingness Measures -- Nonnegative Matrix Factorization: Models, Algorithms and Applications -- Visual Data Mining and Discovery with Binarized Vectors.
Keywords: Engineering; Medical records -- Data processing; Artificial intelligence; Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics).; Health Informatics