采样特异性因子及异常检测

特异性因子是数据的重要特征之一,常通过累计数据之间的差异得到,是面向特异性挖掘的核心概念,然而遇到了计算时间复杂度过高的问题。本文在分析已有特异性因子定义特点及其计算算法时间复杂度的基础上,指出应该基于采样的方法定义特异性因子。给出了一种基于采样的特异性因子定义,即采样特异性因子(sampled peculiarity factor,SPF),并提出了一种基于SPF的异常检测算法。在真实数据集上进行对比实验,结果表明:该算法在检测异常数据时,精度降低不明显,而运行效率得以较大提高,这说明基于采样定义特异性因子的方法可行和更为合理。本文还指出采用合适的采样方法可经进一步优化SPF的计算过程,进而节约占用CPU时间和满足实时性要求高的应用。

Sampled peculiarity factor and its application in anomaly detection

The peculiarity factor (PF), an important feature of data and obtained by accumulating differences between data, is a core concept of peculiarity-oriented mining (POM). But it meets a higher computational time complexity for any algorithm.A sampled approach firstly was suggested to define PF through analyzing current versions of PF and computational complexities of algorithms to compute it. A sampled PF (SPF) was proposed to meet real time requirement and a SPF outlier detection algorithm was given. Experiments using real datasets show that the SPF-outlier detection algorithm is efficient with losing a few of precisions through contrasting with two baseline algorithms and it is a feasible and right approach to define PF by sampling. Furthermore, some right sampling methods could be used to compute SPFs in order to meet real-time requirement.