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This second and revised edition contains a detailed introduction to the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of classical statistical issues. The following chapters concentrate on machine learning and artificial intelligence, rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on visualization and an advanced overview of IDA processes.
- Sales Rank: #2378185 in Books
- Brand: Brand: Springer
- Published on: 2007-02-15
- Original language: English
- Number of items: 1
- Dimensions: 9.57" h x 1.32" w x 6.38" l, 1.85 pounds
- Binding: Hardcover
- 525 pages
- Used Book in Good Condition
Most helpful customer reviews
26 of 27 people found the following review helpful.
Broadly Useful Reference For Intellignet Data Analysis
By Larry Mazlack
This book provides a detailed presentation of several important approaches to intelligent data analysis. It has ten chapters, each chapter written by a different technical specialist. The book could well serve as a text for a graduate level course on data analysis. It also works well as a reference. There are many useful illustrations and examples.
The first part of this book is focused on classical statistical issues. Arguably, anyone seeking to perform advanced data analysis should have a working knowledge of this area. It is my personal observation that, unfortunately, many workers do not. This book provides a good way of gaining a broad understanding of statistical methods. My only caveat is that the discussion of na�ve Bayesian classifiers could have been more extensive. (The chapter on general Bayesian classifiers is other wise well done.) Na�ve Bayesian classifiers have been reasonably successful in machine learning and a more in depth treatment would have been useful.
The later chapters focus on machine learning. They provide useful introductions into: induction, neural networks, fuzzy logic, and stochastic search. These chapters are particularly useful to workers contemplating how to best perform advanced analysis of complex, large, and possibly imprecise data sets. Consequently, someone contemplating data mining or other intelligent data analysis applications should seriously consider acquiring this book.
15 of 15 people found the following review helpful.
Good book for academic work
By John Hofmann
I'm sure this book is very helpful for academics who are doing work or research into sophisticated ways of extracting knowledge from data, but if this is something you are looking to do for a practical or professional reason, this book probably isn't for you. It's very detailed with in-depth mathematical explanations for everything, although they are not helpful in actually implementing any of these types of algorithms. The book is also basically an index into publications and other works, so it's not really self-contained and I don't think it should be considered a standalone work.
It's got very interesting, well-researched material from very knowledgeable academics, and it seems like that's also the target audience. That's not bad, but it wasn't clear when I bought this book. If you're like me, and you're looking for practical explanations of these concepts, you may want to consider looking elsewhere.
I assume it's very useful for pure researchers, although I'm not one of those people so I have no insight into their needs. I hope this review helps give an idea of the contents.
39 of 41 people found the following review helpful.
statistical data analysis, AI and neural nets
By Michael R. Chernick
This is a book by Springer Verlag that came out if 1999. This book introduces a lot of useful statistical tools and has chapters written by statisticians and computer scientists. The editors also contribute. They emphasize useful tools and computer tools. It includes material from the artificial intelligence literature including fuzzy set logic, genetic algorithms and expert systems. There is some discussion of data mining, Bayesian methods and neural networks.
Chapters are written on an elementary level for students and pratictioners of modern data analysis techniques. Written mainly as a text but expanded to cover topics of interest to researchers in statistics and computer science by subject matter experts. The last chapter on Systems and Applications by Xiaohui Liu includes coverage of data quality. Among the references on data quality and outlier detection is the book edited by Wright "Statistical Methods and the Improvement of Data Quality". That book was a collection of papers from a conference held in Oak Ridge Tennessee in 1982. That volume was published by Academic Press in 1983. It is not often sighted in the statistical literature but it did contain a number of interesting papers. I contributed a chapter on influence function methods for outlier detection to the Academic Press book.
Hand has written many books on statistics and especially some excellent texts on classification and pattern recognition. His recent work on data mining was published in 1999 by MIT press, a volume he coauthored with Mannila and Smyth. it is one of teh few data mining texts that is highly regarded by the statistical community. Much of that work in referenced in this book particularly in Chapter 1, the overview chapter on intellegent data analysis that Hand wrote himself.
Resampling methods, generalized linear models, Bayesian methods, time series, multivariate analysis, random effects models and entropy are all covered with nice elementary introductions.
This is a great reference source with over 440 articles and books in the list of references.
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