Mining Imperfect Data
Dealing with Contamination and Incomplete Records
By (author) Ronald K. Pearson
Normal Price: $175.00
Your Price: $157.50 AUD, inc. GST
Shipping: $7.95 per order
You Save: $17.50! (10% off normal price)
Plus...earn $7.88 in Boomerang Bucks
Availability: Available to Backorder, No Due Date for Supply
Mining Imperfect Data by Ronald K. Pearson
Book DescriptionThis book thoroughly discusses the varying problems that occur in data mining, including their sources, consequences, detection, and treatment. Specific strategies for data pretreatment and analytical validation that are broadly applicable are described, making them useful in conjunction with most data mining analysis methods. Examples illustrate the performance of the pretreatment and validation methods in a variety of situations. The book, which deals with a wider range of data anomalies than are usually treated, includes a discussion of detecting anomalies through generalized sensitivity analysis (GSA), a process of identifying inconsistencies using systematic and extensive comparisons of results obtained by analysis of exchangeable datasets or subsets. Real data is made extensive use of, both in the form of a detailed analysis of a few real datasets and various published examples. A succinct introduction to functional equations illustrates their utility in describing various forms of qualitative behavior for useful data characterizations.
Buy Mining Imperfect Data book by Ronald K. Pearson from Australia's Online Bookstore, Boomerang Books.
Book DetailsISBN: 9780898715828
(228mm x 152mm x 16mm)
Imprint: Society for Industrial & Applied Mathematics,U.S.
Publisher: Society for Industrial & Applied Mathematics,U.S.
Publish Date: 1-Apr-2005
Country of Publication: United States
Books By Author Ronald K. Pearson
Discrete-time Dynamic Models, Hardback (April 1999)» View all books by Ronald K. Pearson
Fuelled by advances in computer technology, model-based approaches to the control of industrial processes are now widespread. This text provides practical insight into model selection for chemical processes and emphasizes structures suitable for control system design.
» Have you read this book? We'd like to know what you think about it - write a review about Mining Imperfect Data book by Ronald K. Pearson and you'll earn 50c in Boomerang Bucks loyalty dollars (you must be a member - it's free to sign up!)
Author Biography - Ronald K. Pearson
Ronald K. Pearson is Senior Scientist with ProSanos Corporation and holds an adjunct faculty position at Jefferson Medical College. His primary research interests are in the areas of nonlinear discrete-time dynamical models, exploratory data analysis, and nonlinear digital signal processing. He is author of the book Discrete-Time Dynamic Models (Oxford University Press, 1999) and coauthor of the book Identification and Control Using Volterra Models (Springer, 2001). He has published three encyclopedia articles and approximately 100 journal and conference papers.
Bestselling Books: Our Current Bestsellers | Australia's Hottest 1000 Books | Bestselling Fiction | Bestselling Crime Mysteries and Thrillers | Bestselling Non Fiction Books | Bestselling Sport Books | Bestselling Gardening and Handicrafts Books | Bestselling Biographies | Bestselling Food and Drink | Bestselling History | Bestselling Travel Books | Bestselling School Textbooks & Study Guides | Bestselling Children's General Non-Fiction | Bestselling Young Adult Fiction | Bestselling Children's Fiction | Bestselling Picture Books | Top 100 US Bestsellers
Phone: 1300 36 33 32 (9am-5pm Mon-Fri AEST) - International: +61 2 9960 7998 - Online Form
Address: Boomerang Books, 878 Military Road, Mosman Junction, NSW, 2088
© 2003-2017. All Rights Reserved. Eclipse Commerce Pty Ltd - ACN: 122 110 687 - ABN: 49 122 110 687
For every $20 you spend on books, you will receive $1 in Boomerang Bucks loyalty dollars. You can use your Boomerang Bucks as a credit towards a future purchase from Boomerang Books. Note that you must be a Member (free to sign up) and that conditions do apply.