基于遗传聚类算法的离群点检测

离群点检测是数据挖掘一个重要内容，它为分析各种海量的、复杂的、含有噪声的数据提供了新的方法。对离群数据挖掘几类主要的方法进行了分析和评价，并在此基础上了提出了一种基于遗传聚类的离群点检测算法。该算法结合了遗传算法全局搜索的优点和K-均值方法局部收敛速度快的特点，取得较好效果。实验验证该算法很好地检测到数据集中的离群点，同时还完成了数据集的聚类。具有较好的实用性。

Outlier detection based on genetic algorithm for clustering

Outlier detection,as an important aspect of data mining,provides a new method for analyzing various quantitative,complex and noisy data.In this paper,authors analyze and evaluate several major methods of the outlier data mining,and propose a new outlier detection algorithm which is based on an genetic algorithm for clustering.By integrating with global searching of the genetic algorithm and the good local convergence rate of the K-means algorithm,this algorithm gets a better result.Experiments show that this algorithm not only can detect the outliers in the dataset,but also complete the clustering of the dataset.So it has a good practicality.