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Book Title: Principles of Data Mining|
The author of the book: David J. Hand
Format files: PDF
The size of the: 22.97 MB
ISBN 13: 9780262082907
Edition: Bradford Book
Date of issue: August 17th 2001
Read full description of the books:The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.
The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
Read information about the authorDavid J. Hand is Senior Research Investigator and Emeritus Professor of Mathematics at Imperial College, London, and Chief Scientific Advisor to Winton Capital Management. He is a Fellow of the British Academy, and a recipient of the Guy Medal of the Royal Statistical Society. He has served (twice) as President of the Royal Statistical Society, and is on the Board of the UK Statistics Authority. He has published 300 scientific papers and 25 books: his next book, The Improbability Principle, is due out in February 2014. He has broad research interests in areas including classification, data mining, anomaly detection, and the foundations of statistics. His applications interests include psychology, physics, and the retail credit industry - he and his research group won the 2012 Credit Collections and Risk Award for Contributions to the Credit Industry. He was made OBE for services to research and innovation in 2013.
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