Outlier detection in share index based on data mining

Outliers detection has wide application for financial surveillance. The Traditional outlier detection method is based on statistical models, such as ARMA, ARCH and GARCH, which require special hypotheses, and they are inappropriate to apply to complex financial data, such as high frequency data. This paper introduces a new data mining method to detect outliers for analysis of share index fluctuation. Based on the Voronoi diagram, we propose a novel outlier detection method, which called Voronoi based Outlier Detection (VOD). Experiments show the VOD method performs more efficient and effective against the existing method in outlier detection for financial data.