The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Critical Parameters for Fuzzy Data Mining
Abstract
Research on data mining is increasing at an incessant rate and to improve its effectiveness other techniques have been applied such as fuzzy sets, rough set theory, knowledge representation, inductive logic programming, or high-performance computing. Fuzzy logic due to its proficiency in handling uncertainty has gained its importance in a variety of applications in combination with the use of data mining techniques. In this chapter we take this association a notch further by examining the parameters which allow fuzzy sets and data mining to be combined into what has come to be known as fuzzy data mining. Analyzing and understanding these critical parameters is the main purpose of this chapter, so as to acquire maximum efficiency in applying the same which impelled the authors to work extensively and find out the crucial parameters essential to the application of fuzzy data mining.
Related Content
Tutita M. Casa, Fabiana Cardetti, Madelyn W. Colonnese.
© 2024.
14 pages.
|
R. Alex Smith, Madeline Day Price, Tessa L. Arsenault, Sarah R. Powell, Erin Smith, Michael Hebert.
© 2024.
19 pages.
|
Marta T. Magiera, Mohammad Al-younes.
© 2024.
27 pages.
|
Christopher Dennis Nazelli, S. Asli Özgün-Koca, Deborah Zopf.
© 2024.
31 pages.
|
Ethan P. Smith.
© 2024.
22 pages.
|
James P. Bywater, Sarah Lilly, Jennifer L. Chiu.
© 2024.
20 pages.
|
Ian Jones, Jodie Hunter.
© 2024.
20 pages.
|
|
|