Study of Outlier Mining Algorithms

摘要：

A local outlier mining algorithm is put forward based on the partition of subspaces. The algorithm first divides the data set into disjoint subspaces, using the degree of skewness to measure the pros and cons of the space division, and adopting the particle swarm optimization algorithm to search the optimal partition of subspaces set; then aiming at each optimal partition of subspaces to calculate the local outlier factor SPLOF value of its data object, and take the SPLOF value as the local deviation degree of measuring the data object. Finally adopting the discrimination astronomical spectral data as the data set, experiments verify that the algorithm possesses the excellence of not relying on users’ input parameters, strong flexibility, and efficient operation and so on.

关键词： Outlier Particle Swarm Optimization Subspace

作者: Chen Lei

作者单位: Computer Engineering Department Chongqing Aerospace Polytechnic College

会议类型: 国际会议

会议名称: 2010 3rd International Conference on Advanced Computer Theory and Engineering(2010年第三届先进计算机理论与工程国际会议 ICACTE 2010)

会议地点: 成都

会议语种:英文

页码: 1-4

在线出版日期: 2010-08-20（万方平台首次上网日期，不代表论文的发表时间）