A semi-supervised clustering algorithm for data exploration

被引:0
作者
Bouchachia, A
Pedrycz, W
机构
[1] Univ Klagenfurt, Dept Informat Syst, A-9020 Klagenfurt, Austria
[2] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 2V4, Canada
来源
FUZZY SETS AND SYSTEMS - IFSA 2003, PROCEEDINGS | 2003年 / 2715卷
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper is concerned with clustering of data that is partly labelled. It discusses a semi-supervised clustering algorithm based on a modified fuzzy C-Means (FCM) objective function. Semi-supervised clustering finds its application in different situations where data is neither entirely nor accurately labelled. The novelty of this approach is the fact that it takes into consideration the structure of the data and the available knowledge (labels) of patterns. The objective function consists of two components. The first concerns the unsupervised clustering while the second keeps the relationship between classes (available labels) and the clusters generated by the first component. The balance between the two components is tuned by a scaling factor. The algorithm is experimentally evaluated.
引用
收藏
页码:328 / 337
页数:10
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