Imputing Missing Values for Mixed Numeric and Categorical Attributes Based on Incomplete Data Hierarchical Clustering

被引：0
|
作者：
Feng, Xiaodong [1 ]
Wu, Sen [1 ]
Liu, Yanchi [1 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Econ & Management, Beijing 100083, Peoples R China
来源：
KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT | 2011年 / 7091卷
关键词：
Mixed Numeric and Categorical Attributes; Missing Value Imputation; Hierarchical Clustering; Incomplete Set Mixed Feature Vector;
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：
Missing data imputation is a key issue of data pre-processing in data mining field. Though there are many methods for missing value imputation, almost each of these imputation methods has its limitation and is designed for either numeric attributes or categorical attributes. This paper presents IMIC, a new missing value Imputation method for Mixed numeric and categorical attributes based on Incomplete data hierarchical clustering after the introduction of a new concept Incomplete Set Mixed Feature Vector (ISMFV). The effect of the new method is valuated through the comparison experiment using 3 real data sets from UCI.
引用
收藏
页码：414 / 424
页数：11
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