Titlebook: Data Mining: Foundations and Intelligent Paradigms; VOLUME 2: Statistica Dawn E. Holmes,Lakhmi C. Jain Book 2012 Springer-Verlag Berlin Hei

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Advanced Modelling Paradigms in Data Mining, information. Acquiring and maintaing these repositories relies on mainstream techniques, technology and methodologies. In this book we discuss a number of founding techniques and expand into intelligent paradigms.

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A Visual Environment for Designing and Running Data Mining Workflows in the Knowledge Grid,Knowledge Grid, a service-oriented framework for distributed data mining on the Grid. DIS3GNO supports all the phases of a distributed data mining task, including composition, execution, and results visualization. The paper provides a description of DIS3GNO, some relevant use cases implemented by it, and a performance evaluation of the system.

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Exceptional Model Mining,omehow exceptional. We discuss regression as well as classification models, and define quality measures that determine how exceptional a given model on a subgroup is. Our framework is general enough to be applied to many types of models, even from other paradigms such as association analysis and graphical modeling.

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1868-4394 th informatics is presented in a handbook style.Written by l.There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayes

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Sudeshna Roy,Ibrahim Seaga Shaws are the most popular neural network type, consisting on a feedforward network of processing neurons that are grouped into layers and connected by weighted links. On the other hand, SVM transforms the input variables into a high dimensional feature space and then finds the best hyperplane that mode

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