Discretization Algorithms of Rough Sets Using Clustering

Author

Wu, Chengdong ; Li, Mengxin ; Han, Zhonghua ; Ying Zhang ; Yue, Yong

Author_Institution

Shenyang Univ. of Archit. & Civil Eng.

fYear

2004

fDate

22-26 Aug. 2004

Firstpage

955

Lastpage

960

Abstract

In this paper, hierarchical clustering method is introduced for attribute discretization. It can determine automatically the significant clusters. First, the best classes for discretization are picked from scatter plots of several statistics. Moreover, these classes keep consistent with extracted clusters from dendrograms. By comparison, hierarchical clustering discretization method is typically more effective and advisable among several cluster algorithms with the defect inspection of wood veneer

Keywords

pattern clustering; rough set theory; statistical analysis; attribute discretization; cluster algorithm; dendrograms; discretization algorithm; hierarchical clustering; rough set theory; statistics; Civil engineering; Clustering algorithms; Clustering methods; Frequency; Information entropy; Inspection; Rough sets; Scattering; Set theory; Statistics; attribute discretization; dendrogram; hierarchical clustering; rough set theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Biomimetics, 2004. ROBIO 2004. IEEE International Conference on

Conference_Location

Shenyang

Print_ISBN

0-7803-8614-8

Type

conf

DOI

10.1109/ROBIO.2004.1521914

Filename

1521914

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2248201