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

机译：基于不完整数据层次聚类的混合数值和分类属性的缺失值插补

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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.

机译：丢失数据归因是数据挖掘领域中数据预处理的关键问题。尽管缺失值插补的方法很多，但几乎每种插补方法都有其局限性，并且设计用于数值属性或类别属性。引入新概念不完整集混合特征向量（ISMFV）之后，本文提出了IMIC，一种基于不完整数据层次聚类的混合数字和类别属性缺失值插补方法。通过使用UCI的3个真实数据集进行比较实验，评估了新方法的效果。

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来源
《Knowledge science, engineering and management》|2011年|p.414-424|共11页
会议地点 Irvine CA(US);Irvine CA(US)
作者
Xiaodong Feng; Sen Wu; Yanchi Liu;
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作者单位

School of Economics and Management University of Science and Technology Beijing Beijing, 100083, P.R. China;

School of Economics and Management University of Science and Technology Beijing Beijing, 100083, P.R. China;

School of Economics and Management University of Science and Technology Beijing Beijing, 100083, P.R. China;

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原文格式 PDF
正文语种 eng
中图分类计算技术、计算机技术;
关键词
mixed numeric and categorical attributes; missing value imputation; hierarchical clustering; incomplete set mixed feature vector;

机译：数值和分类属性混合；缺失价值估算层次聚类；不完整集混合特征向量;

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1. A Genetic Algorithm Based Approach for Imputing Missing Discrete Attribute values in Databases [J] . R. DEVI PRIYA, S. KUPPUSWAMI WSEAS Transactions on Information Science and Applications . 2012,第4a6期

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2. A fuzzy c-means-type algorithm for clustering of data with mixed numeric and categorical attributes employing a probabilistic dissimilarity functional [J] . Sotirios P. Chatzis Expert Systems with Application . 2011,第7期

机译：一种采用概率相异函数的具有混合数值和分类属性的数据聚类的模糊c均值类型算法
3. Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number [J] . Cheung Y.-M., Jia H. Pattern Recognition: The Journal of the Pattern Recognition Society . 2013,第8期

机译：基于统一相似性度量的分类和数字属性数据聚类，而无需知道聚类编号
4. Imputing Missing Values for Mixed Numericand Categorical Attributes Based on Incomplete DataHierarchical Clustering [C] . Xiaodong Feng, Sen Wu, Yanchi Liu International Conference on Knowledge Science, Engineering and Management . 2011

机译：基于不完整的DataHirearchary群集抵消混合Numericand分类属性的缺失值
5. A Monte Carlo Study of Missing Data Treatments for an Incomplete Level-2 Variable in Hierarchical Linear Models. [D] . Kwon, Hyukje. 2011

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6. The effect of imputing missing clinical attribute values on training lung cancer survival prediction model performance [O] . Mohamed S. Barakat, Matthew Field, Aditya Ghose, 2017

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7. A Similarity Measurement with Entropy-Based Weighting for Clustering Mixed Numerical and Categorical Datasets [O] . Xia Que, Siyuan Jiang, Jiaoyun Yang, 2021

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8. Hypothesis-Testing Approach to Discriminant Analysis with Mixed Categorical andContinuous Variables When Data are Missing [R] . Miller, J. W., Woodward, W. A., Gray, H. L., 1994

机译：数据丢失时混合分类和连续变量判别分析的假设检验方法

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

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