Principles of Data Mining (Adaptive Computation and Machine Learning)
| Author | : | |
| Rating | : | 4.11 (679 Votes) |
| Asin | : | 026208290X |
| Format Type | : | paperback |
| Number of Pages | : | 578 Pages |
| Publish Date | : | 2014-04-11 |
| Language | : | English |
DESCRIPTION:
Heikki Mannila is Research Fellow at Nokia Research Center and Professor, Department of Computer Science and Engineering, Helsinki University of Technology. Padhraic Smyth is Associate Professor, Department of Information and Computer Science, the University of California, Irvine. . Hand is Professor of Statistics, Department of Mathematics, Imperial College, London. David J
Kevin Nasman said Great book with a great layout!. I'd been struggling with the seemingly infinite ways to approach data mining and this book cleared it all up for me. It is absolutely full of information and is a great base reference. It does not contain complete algorithms or step by step instructions (you can get those anywhere these. Michael R. Chernick said finally a good statistical and computer science perspective on data mining. This book is not an introductory text. Anyone interested in a particular topic should consult the preface of the text to find out what it is about. The negative reviewers were not fair to the authors on that score. Had they read the preface they would have found out (1) how the authors . Very, Bad Book ! I was very disappointed in this book. There are so many other books in the field of Data Mining that are so much better. This one has very little to offer.It does a poor job explaining the theory.It does a poor job giving practical "hands on" advice.SAVE YOUR MONEY, AVOID THIS BOOK !!!
The presentation emphasizes intuition rather than rigor. 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.. 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. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. 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 second section, data mining algorithms, shows how algorithms are constructed to solve spe
Hand is Professor of Statistics, Department of Mathematics, Imperial College, London. About the Author David J. Heikki Mannila is Research Fellow at Nokia Research Center and Professor, Department of Computer Science and Engineering, Helsinki University of Technology. Padhraic Smyth is Associate Professor, Department of Information and Computer Science, the University of California, Irvine.