在许多数据挖掘的实际应用中要求每一个类别的实例数量相对平衡. 而独立子空间聚类的熵加权K-means算法(EWKM)会产生不均衡的划分, 聚类质量很差. 本文定义了一种兼顾平衡划分与特征分布的多目标熵, 然后应用该熵改进了EWKM算法的目标函数, 同利用迭代方法和交替方向乘子法设计其求解流程, 并提出基于熵的平衡子空间K-means算法(EBSKM). 最后, 在UCI、UCR等公开数据集进行聚类实验, 结果表明所提算法在准确率和平衡性方面都优于同类算法.
In many practical applications of data mining, instances for each cluster are often required to be balanced in number. However, the entropy-weighted K-means algorithm (EWKM) for independent subspace clustering leads to unbalanced partitioning and poor clustering quality. Therefore, this study defines a multi-objective entropy that takes balanced partitioning and feature distribution into account and then employs the entropy to improve the objective function of the EWKM algorithm. Furthermore, the study designs the solution process by using the iterative method and alternating direction method of multipliers and proposes the entropy-based balanced subspace K-means algorithm (EBSKM). Finally, the clustering experiments are conducted in public datasets such as UCI and UCR, and the results show that the proposed algorithm outperforms similar algorithms in terms of accuracy and balance.