摘要
为了提高挖掘用户频繁行为模式的速度和FP-树空间利用率,从而显著提高安全审计数据分析的效率,本文在FP-growth算法的基础上提出了一种改进的适于安全审计数据分析的挖掘频繁模式算法。与FP-growth算法相比,改进算法在挖掘频繁模式时不生成条件FP-树,挖掘速度提高了1倍以上,所需的存储空间减少了一半。
In order to improve the speed of mining user frequent behavior pattern and FP-tree space utilization, thereby significantly improving the efficiency of security audit data analysis, based on the FP-growth algorithm this paper proposes an improved correlation algorithm suitable for the analysis of the security audit data. Experiments show that in comparison with FP-growth, the proposed algorithm does not generate conditional FP-tree in mining process and it has accelerated the mining speed by at least two times and reduced the space consumption by half.
出处
《信息工程大学学报》
2007年第1期22-25,共4页
Journal of Information Engineering University
