高维空间中离群点检测算法研究  

Research on Outliers Detection Algorithms in High-Dimensional Space

作　　者：刘培奇[1] 孙靖[1] 段中兴[1] 

机构地区：[1]西安建筑科技大学信息与控制工程学院,陕西西安710055

出　　处：《微电子学与计算机》2013年第7期68-71,77,共5页Microelectronics & Computer

基　　金：国家自然科学基金项目(51178373);陕西省教育厅自然科学计划项目(12JK0743)

摘　　要：提出一种基于改进粒子群优化算法的离群点检测算法,解决高维环境下离群点挖掘效率偏低的问题.新算法能够充分发挥粒子群优化算法全局搜索的优势,并具有k均值算法快速收敛的特点,可避免粒子群优化算法的早熟,减小确定k均值算法聚类中心的计算量等问题.实验表明,该算法在高维环境下可快速有效的挖掘出离群数据的离群支持度,有较好的挖掘效率、准确率和实用性.A kind of outlier detection algorithm based on the improved particle swarm optimization algorithm is proposed in the paper here.And the paper focuses on solving the problem that mining algorithms had low efficiency in high dimensional environment.New algorithm using the weight of evolutionary algorithm and moving step function improve particle swarm optimization algorithm,and new algorithm unite k-means algorithm in it.At the same time,the algorithm uses the global search of particle swarm optimization algorithm and the rapid convergence of k-means algorithm advantages.So the algorithms can avoid the premature convergence of the particle swarm optimization algorithm and reduce the amount of calculation clustering center for the k-means algorithm.Experiments show that this algorithm,in high dimensional environment,can mining outliers the support degree of outlier data,which making outlier detection efficiently,accurately and practicably.

关 键 词：数据挖掘 离群点检测 高维数据 PSO算法 K均值算法 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]