Mixture model clustering for mixed data with missing information
Mixture model clustering for mixed data with missing information
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DOI：
10.1016/s0167-9473(02)00190-1
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发表时间：
2003-01-28
影响因子：
1.8
通讯作者：
Jorgensen, M
中科院分区：
文献类型：
--
作者：
Hunt, L;Jorgensen, M
分类研究的一大困难是多变量数据集中经常出现的未观察到或缺失的观察结果。聚类的混合似然方法已经得到很好的发展并且被广泛使用，特别是对于成分分布为多元正态的混合。结果表明，这种方法可以扩展到分析具有混合分类和连续属性的数据，并且在 Little 和 Rubin 的意义上随机丢失一些数据（混合数据统计分析，Wiley，纽约）。 (C) 2002 Elsevier Science B.V. 保留所有权利。
One difficulty with classification studies is unobserved or missing observations that often occur in multivariate datasets. The mixture likelihood approach to clustering has been well developed and is much used, particularly for mixtures where the component distributions are multivariate normal. It is shown that this approach can be extended to analyse data with mixed categorical and continuous attributes and where some of the data are missing at random in the sense of Little and Rubin (Statistical Analysis with Mixing Data, Wiley, New York). (C) 2002 Elsevier Science B.V. All rights reserved.