关键词: 离群点检测, 数据挖掘, 遗传算法, 聚类, K-均值算法

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