A New Algorithm for High-Dimensional Outlier Detection Based on Constrained Particle Swarm Intelligence

摘要：

In this paper we present an algorithm for outlier detection in high-dimensional spaces based on constrained particle swarm optimization techniques.The concept of outliers is defined as sparsely populated patterns in lower dimensional subspaces.The search for best abnormally sparse subspaces is done by an innovative use of particle swarm optimization methods with a specifically designed particle coding and conversion strategy as well as some dimensionality-preserving updating techniques.Experimental results show that the proposed algorithm is feasible and effective for high-dimensional outlier detection problems.

作者: Dongyi Ye Zhaojiong Chen

作者单位: College of Mathematics and Computer,Fuzhou University,Fuzhou 350002,China

会议类型: 国际会议

会议名称: The Third International Conference on Rough Sets and Knowledge Tevhnology(RSKT 2008)(第三届粗糙集与知识技术国际会议)

会议地点: 成都

会议语种:英文

页码: 516-523

在线出版日期: 2008-05-17（万方平台首次上网日期，不代表论文的发表时间）