基于累积全熵的子空间聚类离群点检测算法

Outlier detection algorithm for subspace clustering based on cumulative holoentropy

针对累积互信息方法存在的最佳聚类子空间选择不稳定和计算复杂的问题,给出累积熵的链式法则、累积全相关、累积全熵的计算方法,采用累积全熵在连续数据集上挖掘最佳聚类子空间,并在最佳聚类子空间中进行离群点挖掘,提出基于累积全熵的子空间聚类离群点检测算法。分别在真实数据集和虚拟数据集上验证了所提算法的有效性和可伸缩性。实验表明,所提算法进一步提高了子空间离群点的挖掘效率。

Aiming at the instability and complex computation of optimal subspace clustering selection existed in Cumulative Mutual Information （CMI） method, the computational method for chain rule of cumulative entropy, cumulative total correlation and cumulative holoentropy were given. Cumulative holoentropy was used to mine the best clustering subspaces on continuous data sets in which outliers were detected, and then subspace outlier detection algorithm based on cumulative holoentropy was proposed. The validity and scalability of proposed method were tested on real datasets and virtual datasets. Experiment result showed that the efficiency of mining outliers in subspaces was enhanced by the proposed algorithm.