- Hardcover: 525 pages
- Publisher: Springer; 2nd edition (February 15, 2007)
- Language: English
- ISBN-10: 3540430601
- ISBN-13: 978-3540430605
- Product Dimensions: 6.4 x 1.3 x 9.6 inches
- Shipping Weight: 1.8 pounds (View shipping rates and policies)
- Average Customer Review: 4 customer reviews
Amazon Best Sellers Rank:
#2,589,670 in Books (See Top 100 in Books)
- #566 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Computer Vision & Pattern Recognition
- #873 in Books > Textbooks > Computer Science > Artificial Intelligence
- #998 in Books > Computers & Technology > Networking & Cloud Computing > Network Administration > Storage & Retrieval
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Intelligent Data Analysis 2nd Edition
Use the Amazon App to scan ISBNs and compare prices.
Fulfillment by Amazon (FBA) is a service we offer sellers that lets them store their products in Amazon's fulfillment centers, and we directly pack, ship, and provide customer service for these products. Something we hope you'll especially enjoy: FBA items qualify for FREE Shipping and Amazon Prime.
If you're a seller, Fulfillment by Amazon can help you increase your sales. We invite you to learn more about Fulfillment by Amazon .
The Amazon Book Review
Author interviews, book reviews, editors picks, and more. Read it now
What other items do customers buy after viewing this item?
From the reviews of the second edition:
"One excellent feature of the second addition … . This is a particularly nice overview with excellent descriptions and numerous illustrations, most in color, for a wide variety of types of visualizations. " (E. Ziegel, Technometrics, 2005)
"In this second edition … have expanded the coverage of topics and ensured that this remains the key text for surveying the field. The twelve chapters which make up the book provide an academically rigorous and concise to the key methodologies which make up the discipline. … In all this is a comprehensive survey of the field, and will appeal to graduate and post-graduate students, researchers and academics seeking an overview of the theoretical tools available for intelligently analyzing large, complex data sets." (TechBookReport, November, 2003)
From the Publisher
This monograph is a detailed introductory presentation of the key classes of intelligent data analysis methods. The ten 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, ranging from the basic concepts of probability, through general notions of inference, to advanced multivariate and time series methods, as well as a detailed discussion of the increasingly important Bayesian approach. The following chapters then concentrate on the area of machine learning and artificial intelligence and provide introductions into the topics of rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a higher level overview of the IDA process and illustrations of the breadth of application of the ideas.
Fields: Artificial Intelligence; Statistics, general; Information Systems/Business Data Processing
Written for: Professionals, students, researchers --This text refers to an alternate Hardcover edition.
Top customer reviews
There was a problem filtering reviews right now. Please try again later.
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.
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 naïve Bayesian classifiers could have been more extensive. (The chapter on general Bayesian classifiers is other wise well done.) Naïve 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.
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.