The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Clustering Mixed Incomplete Data
|
|
Author(s): Jose Ruiz-Shulcloper (University of Tennessee, USA), Guillermo Sanchez-Diaz (Autonomous University of Hidalgo State, Mexico)and Mongi A. Abidi (University of Tennessee, USA)
Copyright: 2002
Pages: 18
Source title:
Heuristic and Optimization for Knowledge Discovery
Source Author(s)/Editor(s): Hussein A. Abbass (University of New South Wales, Australia), Charles S. Newton (University of New South Wales, Australia)and Ruhul Sarker (University of New South Wales, Australia)
DOI: 10.4018/978-1-930708-26-6.ch006
Purchase
|
Abstract
In this chapter, we expose the possibilities of the Logical Combinatorial Pattern Recognition (LCPR) tools for Clustering Large and Very Large Mixed Incomplete Data (MID) Sets. We start from the real existence of a number of complex structures of large or very large data sets. Our research is directed towards the application of methods, techniques and in general, the philosophy of the LCPR to the solution of supervised and unsupervised classification problems. In this chapter, we introduce the GLC and DGLC clustering algorithms and the GLC+ clustering method in order to process large and very large mixed incomplete data sets.
Related Content
|
Murray Eugene Jennex.
© 2020.
29 pages.
|
|
Ronald John Lofaro.
© 2020.
18 pages.
|
|
Mark E. Nissen.
© 2020.
23 pages.
|
|
Ronel Davel, Adeline S. A. Du Toit, Martie Mearns.
© 2020.
32 pages.
|
|
Murray Eugene Jennex.
© 2020.
23 pages.
|
|
Michael J. Zhang.
© 2020.
21 pages.
|
|
Toshali Dey, Susmita Mukhopadhyay.
© 2020.
23 pages.
|
|
|