现有粗糙K-means聚类算法及系列改进、衍生算法均是从不同角度描述交叉类簇边界区域中的不确定性数据对象,却忽视类簇间规模的不均衡对聚类迭代过程及结果的影响.文中引入区间2-型模糊集的概念度量类簇的边界区域数据对象,提出基于区间2-型模糊度量的粗糙K-means聚类算法.首先根据类簇的数据分布生成边界区域样本对交叉类簇的隶属度区间,体现数据样本的空间分布信息.然后进一步考虑类簇的数据样本规模,在隶属度区间的基础上自适应地调整边界区域的样本对交叉类簇的影响系数.文中算法削弱边界区域对较小规模类簇的中心均值迭代的不利影响,提高聚类精度.在人工数据集及UCI标准数据集的测试分析验证算法的有效性.%The rough k-means algorithm and its derivatives focus on the description of data objects in uncertain boundary areas. However,the influence of imbalanced sizes between clusters on the clustering result is ignored. The interval type-2 fuzzy measure is introduced in this paper for measuring the boundary objects and an improved rough K-means clustering algorithm is developed. Firstly, the membership degree interval of the boundary object is calculated according to the data distribution of clusters and thus the spatial distribution of clusters is described. Then, the data sample size of the cluster is taken into account to adaptively adjust the influence coefficient of boundary objects on overlapping clusters. The experimental results on both synthetic and UCI datasets show that the adverse impact of the boundary objects on the means iterative calculations of small sample size clusters is mitigated and the clustering accuracy is improved.
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