Application of self-organising maps for data mining with incomplete data sets

被引：45
作者：
Wang, SH [1 ]
机构：
[1] Univ Massachusetts Dartmouth, Charlton Coll Business, Dept Mkt Business Informat Syst, N Dartmouth, MA 02747 USA
来源：
NEURAL COMPUTING & APPLICATIONS | 2003年 / 12卷 / 01期
关键词：
fuzzy clustering; incomplete data; self-organising maps;
D O I：
10.1007/s00521-003-0372-1
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Self-organising maps (SOM) have become a commonly-used cluster analysis technique in data mining. However, SOM are not able to process incomplete data. To build more capability of data mining for SOM, this study proposes an SOM-based fuzzy map model for data mining with incomplete data sets. Using this model, incomplete data are translated into fuzzy data, and are used to generate fuzzy observations. These fuzzy observations, along with observations without missing values, are then used to train the SOM to generate fuzzy maps. Compared with the standard SOM approach, fuzzy maps generated by the proposed method can provide more information for knowledge discovery.
引用
收藏
页码：42 / 48
页数：7
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