基于改进布谷鸟搜索的k-means算法的离群点检测

为了解决k-means算法的离群点检测容易受到初始聚类中心的影响陷入局部最优的问题，本文提出一种基于改进布谷鸟搜索的k-means算法的离群点检测方法。首先，对原始布谷鸟搜索算法中的发现概率和莱维飞行步长做自适应策略改进并进行实验仿真；其次讨论改进后的布谷鸟搜索算法的收敛性问题；最后将改进后的布谷鸟搜索算法与k-means的离群点检测算法融合成一种新的离群点检测算法——基于改进布谷鸟搜索的k-means算法的离群点检测。通过对UCI数据集进行仿真实验，结果表明，本文算法不仅精确度方面有着明显优势，而且在3个数据集上收敛速度均有改善，可有效地抑制k-means算法的离群点检测容易陷入局部最优的问题，缩短运行时间。

Outlier Detection Based on Improved Cuckoo Search k-means Algorithm

In order to solve the problem that the outlier detection of k-means algorithm is susceptible to fall into local optimality by the influence of the initial clustering center, an outlier detection based on the k-means algorithm of improving cuckoo search is proposed. Firstly, the adaptive strategy improvement is made to the discovery probability and Levy flight step size of the original cuckoo search algorithm, and the experimental simulation is carried out. Secondly, the convergence of the improved cuckoo search algorithm is discussed. Finally, the improved cuckoo search algorithm and the k-means outlier detection algorithm are fused into a new outlier detection algorithm: the outlier detection method based on the k-means algorithm of improved cuckoo search. Through the simulation experiments on UCI data sets, the results show that the proposed algorithm not only has obvious advantages in accuracy, but also improves the convergence speed on three data sets, which can effectively suppress the problem that the outlier detection of k-means algorithm is easy to fall into local optimality and shorten the running time.