Abstract: 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.

Key words: outlier detection, data mining, genetic algorithm, clustering, K-means algorithm

摘要： 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。

关键词: 离群点检测, 数据挖掘, 遗传算法, 聚类, K-均值算法