中文摘要：

为了同时对数据对象和特征进行聚类分析以提高聚类准确率，在模糊紧致性和分离性算法（fuzzycompactnessandseparation，FCS）基础上，提出一种结合类内紧致性和类间分离性的模糊联合聚类算法（fuzzycompactnessandseparationCO．clustering，FCSCC）。该算法在FCS的基础上增加了对特征维度的隶属度关系与熵最大化原理，能够在数据对象和特征2个维度上同时聚类。为验证该算法的有效性，另选择了3种算法在5个数据集上进行了对比实验，结果表明，FCSCC算法的聚类准确率高于其他3种算法。

英文摘要：

In order to implement clustering on feature dimension as well as data object dimension and improve clustering accuracy,a fuzzy compactness and separation co-clustering algorithm（FCSCC） is proposed based on the fuzzy compactness and separation algorithm （FCS）. In FCSCC ,feature membership and entropy maximiza- tion is added into FCS. It can simultaneously group data objects and features. In order to evaluate clustering ef- fectiveness,experiments were carried out on five datasets to compare the FCSCC with other three clustering al- gorithms. The experimental results show that the FCSCC algorithm is better than these three methods in terms of accuracy.