DocumentCode
3021496
Title
Study on Comparison of Discretization Methods
Author
Peng, Liu ; Qing, Wang ; Yujia, Gu
Author_Institution
Sch. of Inf. Manage. & Eng., Shanghai Univ. of Finance & Econ., Shanghai, China
Volume
4
fYear
2009
fDate
7-8 Nov. 2009
Firstpage
380
Lastpage
384
Abstract
Discrete features play an important role in data mining. How to best discretize continuous features has always been a NP-hard problem. This paper introduces diverse taxonomies in the existing literature to classify discretization methods, as well as idea and drawbacks of some typical methods. Furthermore, a comparison of these methods is studied. It´s essential to select proper methods depending on learning environment. At last, the thought of choosing the best discretization methods in association analysis is proposed as future research.
Keywords
data mining; learning (artificial intelligence); optimisation; NP-hard problem; data mining; discrete features; discretization methods; Arithmetic; Artificial intelligence; Association rules; Computational intelligence; Data mining; Information management; Machine learning; Machine learning algorithms; NP-hard problem; Taxonomy; continuous features; discrete features; discretization;
fLanguage
English
Publisher
ieee
Conference_Titel
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-4244-3835-8
Electronic_ISBN
978-0-7695-3816-7
Type
conf
DOI
10.1109/AICI.2009.385
Filename
5376309
Link To Document