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Instance Selection and Construction for Data Mining by Hiroshi Motoda, Huan Liu (Hardcover, 2001)

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The ability to analyze and understand massive data sets lags far behind the ability to gather and store the data. To meet this challenge, knowledge discovery and data mining (KDD) is growing rapidly as an emerging field. However, no matter how powerful computers are now or will be in the future, KDD researchers and practitioners must consider how to manage ever-growing data which is, ironically, due to the extensive use of computers and ease of data collection with computers. Many different approaches have been used to address the data explosion issue, such as algorithm scale-up and data reduction. Instance, example, or tuple selection pertains to methods or algorithms that select or search for a representative portion of data that can fulfill a KDD task as if the whole data is used. Instance selection is directly related to data reduction and becomes increasingly important in many KDD applications due to the need for processing efficiency and/or storage efficiency. One of the major means of instance selection is sampling whereby a sample is selected for testing and analysis, and randomness is a key element in the process. Instance selection also covers methods that require search. Examples can be found in density estimation (finding the representative instances - data points - for a cluster); boundary hunting (finding the critical instances to form boundaries to differentiate data points of different classes); and data squashing (producing weighted new data with equivalent sufficient statistics). Other important issues related to instance selection extend to unwanted precision, focusing, concept drifts, noise/outlier removal, and data smoothing. This title brings researchers and practitioners together to report new developments and applications, to share hard-learned experiences in order to avoid similar pitfalls, and to shed light on the future development of instance selection. This volume serves as a comprehensive reference for graduate students, practitioners and researchers in KDD.

Product Identifiers

Publisher
Springer
ISBN-13
9780792372097
eBay Product ID (ePID)
94319764

Product Key Features

Subject Area
Information Science
Author
Hiroshi Motoda, Huan Liu
Publication Name
Instance Selection and Construction for Data Mining
Format
Hardcover
Language
English
Subject
Computer Science, Mathematics
Publication Year
2001
Type
Textbook
Number of Pages
416 Pages

Dimensions

Item Height
235mm
Item Width
155mm
Volume
608
Item Weight
1780g

Additional Product Features

Series Title
The Springer International Series in Engineering and Computer Science
Editor
Hiroshi Motoda, Huan Liu
Country/Region of Manufacture
Netherlands

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Instance Selection and Construction for Data Mining by Hiroshi Motoda, Huan Liu (Hardcover, 2001)

About this product

Product Information

Product Identifiers

Product Key Features

Dimensions

Additional Product Features

Best Selling in Adult Learning & University

Permaculture: A Designer's Manual by Bill Mollison (Hardcover, 1988)

Australian Native Bee Book: Keeping Stingless Bee Hives for Pets, Pollination and Sugarbag Honey by Tim Heard (Paperback, 2015)

The Weed Forager's Handbook: A Guide to Edible and Medicinal Weeds in Australia by Adam Grubb, Annie Raser-Rowland (2012, Paperback)

Empire of the Summer Moon: Quanah Parker and the Rise and Fall of the Comanches, the Most Powerful Indian Tribe in American History by S.C. Gwynne (Paperback, 2011)

Botanical Field Guide by Stefan Mager (Fold-out book or chart, 2021)

Linux Basics For Hackers: Getting Started with Networking, Scripting, and Security in Kali by OccupyTheWeb (Paperback, 2018)

The Hundred Years' War on Palestine: A History of Settler Colonial Conquest and Resistance by Rashid I. Khalidi (Paperback, 2020)

Save on Adult Learning & University

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