Statistical Computing

With a focus on computing system management, this book presents a variety of event mining approaches for improving the quality and efficiency of IT service and system management. It covers different components in the data-driven framework, from system monitoring and event generation to pattern disco..

Extending R covers key concepts and techniques in R to support analysis and research projects. It provides programming guidance for projects of all scales, and introduces new, valuable techniques that extend R. and incorporates a new structure for interfaces applicable to a variety of languages...

Graphical methods for analysing data are presented in this encompassing text. Some methods are new and some are old, some require a computer and others only paper and pencil; but they are all powerful data analysis tools. In many situations, a set of data even a large set - can be adequately analys..

This handbook shows readers how best to manage and manipulate data by using the DATA step in SAS, helping them avoid common problems when creating SAS data sets. The author explains that learning syntax does not solve all problems; rather, a thorough comprehension of SAS processing is needed for suc..

Learning with uncertainty covers a broad range of scenarios in machine learning, this book mainly focuses on: (1) Decision tree learning with uncertainty, (2) Clustering under uncertainty environment, (3) Active learning based on uncertainty criterion, and (4) Ensemble learning in a framework of unc..

Incorporating state-of-the-art techniques, this book provides complete theoretical and practical treatment of manifold learning. An excellent entry point for readers new to the subject, it supplies a high-level introductory view of the topic as well as in-depth discussion of the key technical detail..

First published in 2011. Routledge is an imprint of Taylor & Francis, an informa company...

This book provides practical guidance on multiple imputation analysis, from simple to complex problems using real and simulated data sets. Data sets from cross-sectional, retrospective, prospective and longitudinal studies, randomized clinical trials, complex sample surveys are used to illustrate bo..

Suitable for graduate students and researchers in statistics and biostatistics as well as those in the medical field, epidemiology, and social sciences, this book introduces univariate survival analysis and extends it to the multivariate case. It also covers competing risks and counting processes an..

This book provides a comprehensive overview of music data analysis, from introductory material to advanced concepts. It covers various applications including transcription and segmentation as well as chord and harmony, instrument and tempo recognition. It also discusses the implementation aspects of..

Written by leaders in the data mining community, including the developers of the RapidMiner software, this book provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sect..

A culmination of the authors’ years of extensive research on the topic, this book provides a complete, self-contained introduction to advances in relational data clustering. It presents a novel theoretical framework as well as the most state-of-the-art models for relational data clustering. The auth..