You are using a new version of the IGI Global website. If you experience a problem, submit a ticket to
helpdesk@igi-global.com
, and continue your work on the
old website
.
Shopping Cart
Login
Register
Language:
English
US
China
All Products
All Products
Books
Journals
Videos
Book Chapters
Journal Articles
Video Lessons
Teaching Cases
Special Offers
Acquire a Source of Open Access (OA) APC Funding for Your Institution Through
IGI Global's OA Fee Waiver (Offset Model) Initiative
For any library that invests in IGI Global's InfoSci-Books and/or InfoSci-Journals databases, IGI Global will match the library’s investment with a fund of equal value to go toward subsidizing the OA APCs for their faculty patrons when their work is submitted/accepted under OA into an IGI Global journal.
Learn More
Subscribe to the Latest Research Through IGI Global's InfoSci-OnDemand Plus
InfoSci®-OnDemand Plus, a subscription-based service, provides researchers the ability to access full-text content from over 100,000+ peer-reviewed book chapters and 25,000+ scholarly journal articles that spans across 350+ topics in 11 core subjects. Users can select articles or chapters that meet their interests and gain access to the full content permanently in their
personal online InfoSci-OnDemand Plus library
.
Subscribe
Purchase the Encyclopedia of Information Science and Technology, Fourth Edition
and Receive Complimentary E-Books of Previous Editions
When ordering directly through IGI Global's Online Bookstore, receive the complimentary e-books for the first, second, and third editions with the purchase of the Encyclopedia of Information Science and Technology, Fourth Edition e-book.
Purchase
Create a Free IGI Global Library Account to Receive an Additional 5% Discount on All Purchases
Exclusive benefits include one-click shopping, flexible payment options, free COUNTER 5 reports and MARC records, and a 5% discount on single all titles, as well as the award-winning InfoSci
®
-Databases.
Sign Up Now!
Receive a 20% Discount on All Publications Purchased Through
IGI Global’s Online Bookstore
This discount cannot be combined with any other offer and is only valid when purchasing directly through IGI Global. (Exclusion of select titles and products may apply).
Browse Publications
Books
Journals
InfoSci
®
-Databases
Articles/Chapters
Publish
with Us
Resources
Librarians
InfoSci
®
-Databases
Book Title List
Journal Title List
Video Title List
Consortia Partnerships
Library and Publisher Collaborations
Product Distributors
Catalogs
Library Account Program
Instructors
Course Adoption
Teaching Cases
Researchers
Browse Books
Browse Journals
Search Open Access Content
Streaming Videos
OnDemand Downloads
Webinars
Authors and Editors
eEditorial Discovery
®
System
Peer Review Process
Ethics and Malpractice
Fair Use Policy
Open Access Publishing
Editorial Services
FAQ
Distributors
Distributor Resources
Book Distributors
Journal Subscription Agencies
E-Resource Partners
Catalogs
About Us
Newsroom
Buy Instant PDF Access
Qty:
1
2
3
4
5
6
7
8
9
10
$37.50
Add to Cart
Available.
Instant access upon order completion.
Share
Recommend to a Librarian
Recommend to a Colleague
Free Content
Sample PDF
More Information
Access on Platform
Favorite
Cite Chapter
Cite Chapter
MLA
Ali, ABM Shawkat. "K-means Clustering Adopting rbf-Kernel."
Data Mining and Knowledge Discovery Technologies.
IGI Global, 2008. 118-142. Web. 13 Dec. 2019. doi:10.4018/978-1-59904-960-1.ch006
APA
Ali, A. S. (2008). K-means Clustering Adopting rbf-Kernel. In D. Taniar (Ed.),
Data Mining and Knowledge Discovery Technologies
(pp. 118-142). Hershey, PA: IGI Global. doi:10.4018/978-1-59904-960-1.ch006
Chicago
Ali, ABM Shawkat. "K-means Clustering Adopting rbf-Kernel." In
Data Mining and Knowledge Discovery Technologies,
ed. David Taniar, 118-142 (2008), accessed December 13, 2019. doi:10.4018/978-1-59904-960-1.ch006
Export Reference
Available In
InfoSci-Books
Science, Engineering, and Information Technology
Library Science, Information Studies, and Education
InfoSci-Computer Science and Information Technology
InfoSci-Library and Information Science
Advances in Data Warehousing and Mining
InfoSci-Select
K-means Clustering Adopting rbf-Kernel
ABM Shawkat Ali (Central Queensland University, Australia)
Source Title:
Data Mining and Knowledge Discovery Technologies
Copyright:
© 2008
|
Pages:
25
DOI:
10.4018/978-1-59904-960-1.ch006
OnDemand PDF Download:
$37.50
Abstract
Clustering technique in data mining has received a significant amount of attention from machine learning community in the last few years as one of the fundamental research area. Among the vast range of clustering algorithm, K-means is one of the most popular clustering algorithm. In this research we extend K-means algorithm by adding well known radial basis function (rbf) kernel and find better performance than classical K-means algorithm. It is a critical issue for rbf kernel, how can we select a unique parameter for optimum clustering task. This present chapter will provide a statistical based solution on this issue. The best parameter selection is considered on the basis of prior information of the data by Maximum Likelihood (ML) method and Nelder-Mead (N-M) simplex method. A rule based meta-learning approach is then proposed for automatic rbf kernel parameter selection.We consider 112 supervised data set and measure the statistical data characteristics using basic statistics, central tendency measure and entropy based approach. We split this data characteristics using well known decision tree approach to generate the rules. Finally we use the generated rules to select the unique parameter value for rbf kernel and then adopt in K-means algorithm. The experiment has been demonstrated with 112 problems and 10 fold cross validation methods. Finally the proposed algorithm can solve any clustering task very quickly with optimum performance.
Complete Chapter List
Search this Book:
Reset