ASTRONOMY · DATA PROCESSING
Fionn Murtagh
Also known as: Fionn D. Murtagh
Fionn Murtagh, BA, BAI, MSc(Dubl), PhD(Paris), Habil.(Strasbourg), is a member of the Royal Irish Academy. Following roles as Head of Department and Head of School in other universities, Fionn Murtagh is Professor of Data Science at the School of Computing and Engineering in the University of Huddersfield, West Yorkshire, England, and is Director of, to be established, the Institute of Mathematics and Data Science at the University of Huddersfield. Source: [Royal Irish Academy](
"May you live in interesting times!" ran the old Chinese wish.
Most acclaimed
Intelligent information retrieval
Intelligent information Retrieval comprehensively surveys scientific information retrieval, which is characterized by growing convergence of information expressed in varying complementary forms of data - textual, numerical, image, and graphics; by the fundamental transformation which the scientific library is currently being subjected to; and by computer networking which as become an essential element of the research fabric. Intelligent Information Retrieval addresses enabling technologies, so-called `wide area network resource discovery tools', and the state of the art in astronomy and other sciences. This work is essential reading for astronomers, scientists in related disciplines, and all those involved in information storage and retrieval.
Multivariate data analysis
Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set of general and astronomical bibliographic references follows each chapter, providing valuable entry-points for research workers in all astronomical sub-disciplines. Although the applications considered focus on astronomy, the algorithms used can be applied to similar problems in other branches of science. Fortran programs are provided for many of the methods described.
Correspondence Analysis and Data Coding with Java and R (Chapman & Hall Computer Science and Data Analysis)
"Developed by Jean-Paul Benzerci more than 30 years ago, correspondence analysis as a framework for analyzing data quickly found widespread popularity in Europe. The topicality and importance of correspondence analysis continue, and with the tremendous computing power now available and new fields of application emerging, its significance is greater than ever." "Correspondence Analysis and Data Coding with Java and R clearly demonstrates why this technique remains important and in the eyes of many, unsurpassed as an analysis framework. After presenting some historical background, the author presents a theoretical overview of the mathematics and underlying algorithms of correspondence analysis and hierarchical clustering. The focus then shifts to data coding, with a survey of the widely varied possibilities correspondence analysis offers and introduction of the Java software for correspondence analysis, clustering, and interpretation tools. A chapter of case studies follows, wherein the author explores applications to areas such as shape analysis and time-evolving data. The final chapter reviews the wealth of studies on textual content as well as textual form, carried out by Benzerci and his research lab. These discussions show the importance of correspondence analysis to artificial intelligence as well as to stylometry and other fields." "This book not only shows why correspondence analysis is important, but with a clear presentation replete with advice and guidance, also shows how to put this technique into practice. Downloadable software and data sets allow quick, hands-on exploration of innovative correspondence analysis applications."--Jacket.