Interval-Valued Examples Learning Based on Fuzzy C-Mean Clustering

Author

Chen, Ming-zhi ; Chen, Guo-Long ; Chen, Shui-Li

Author_Institution

Coll. of Math. & Comput. Sci., Fuzhou Univ.

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

1153

Lastpage

1158

Abstract

In this paper, a new approach to generate decision tree from those examples with interval-valued attributes is presented and then rule matching is made. Considering that the interval values of the same attribute of all examples probably fall into certain distributing rule so as to form some center points, we cluster the interval-valued attributes of all examples by using the algorithm of FCCID (fuzzy c-mean clustering for interval-valued data). Consequently, the attributes represented by interval data are transformed into those represented by fuzzy degree of membership. On the basis of that, the fuzzy ID3 algorithm is adopted to generate a decision tree for rule matching

Keywords

fuzzy set theory; learning by example; pattern clustering; FCCID; decision tree; fuzzy ID3 algorithm; fuzzy c-mean clustering; interval-valued examples learning; rule matching; Clustering algorithms; Computer science; Cybernetics; Decision trees; Educational institutions; Fuzzy reasoning; Fuzzy sets; Information entropy; Machine learning; Partitioning algorithms; Roentgenium; Interval-valued data; fuzzy ID3; fuzzy clustering; learning from examples;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258596

Filename

4028237