史倩玉, 梁吉业, 赵兴旺. 一种不完备混合数据集成聚类算法[J]. 计算机研究与发展, 2016, 53(9): 1979-1989.
 引用本文: 史倩玉, 梁吉业, 赵兴旺. 一种不完备混合数据集成聚类算法[J]. 计算机研究与发展, 2016, 53(9): 1979-1989.
Shi Qianyu, Liang Jiye, Zhao Xingwang. A Clustering Ensemble Algorithm for Incomplete Mixed Data[J]. Journal of Computer Research and Development, 2016, 53(9): 1979-1989.
 Citation: Shi Qianyu, Liang Jiye, Zhao Xingwang. A Clustering Ensemble Algorithm for Incomplete Mixed Data[J]. Journal of Computer Research and Development, 2016, 53(9): 1979-1989.

## A Clustering Ensemble Algorithm for Incomplete Mixed Data

• 摘要: 集成聚类技术由于具有较好的泛化能力，目前引起了研究者的高度关注.已有研究主要关注数值型完备数据的集成聚类问题.然而，实际应用中面临的数据往往是兼具数值属性和分类属性共同描述的混合型数据，而且通常带有缺失值.为此，针对不完备混合数据提出了一种集成聚类算法，首先利用3种缺失值填充方法对不完备混合数据进行完备化处理；其次在3种填充后的不同完备数据集上分别多次执行K-Prototypes算法产生基聚类结果；最后对基聚类结果进行集成.在UCI真实数据集上与传统聚类算法通过实验进行了比较分析，实验结果表明提出的算法是有效的.

Abstract: Cluster ensembles have recently emerged a powerful clustering analysis technology and caught high attention of researchers due to their good generalization ability. From the existing work, these techniques held great promise, most of which generate the final results for complete data sets with numerical attributes. However, real life data sets are usually incomplete mixed data described by numerical and categorical attributes at the same time. And these existing algorithms are not very effective for an incomplete mixed data set. To overcome this deficiency, this paper proposes a new clustering ensemble algorithm which can be used to ensemble final clustering results for mixed numerical and categorical incomplete data. Firstly, the algorithm conducts completion of incomplete mixed data using three different missing value filling methods. Then, a set of clustering solutions are produced by executing K-Prototypes clustering algorithm on three different kinds of complete data sets multiple times, respectively. Next, a similarity matrix is constructed by considering all the clustering solutions. After that, the final clustering result is obtained by hierarchical clustering algorithms based on the similarity matrix. The effectiveness of the proposed algorithm is empirically demonstrated over some UCI real data sets and three benchmark evaluation measures. The experimental results show that the proposed algorithm is able to generate higher clustering quality in comparison with several traditional clustering algorithms.

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