摘要：
In order to reduce the influence of errata in the original data and improve the data quality, we deeply analyze the commonly used distance-based outlier detection algorithm, and propose a new outlier detection algorithm based on the improved distance, omitting the setting of DB (d, p) parameter in the traditional algorithm. First of all, in order to solve the problem of terminal uncertainty selection attributes, the concept of "attribute membership degree" is introduced to simplify the selection of detection attributes. Secondly, in order to solve the problem of low detection accuracy caused by uneven data distribution, the commonly used distance measurement is improved, and the improved weighted distance is used for calculation to obtain the distance matrix. By analyzing the total value of the calculated distance, an anomaly evaluation method is proposed to judge the anomaly degree of the abnormal points. The experiment is conducted with the stock trading data. Compared with traditional distance-based detection algorithm, it shows that the improved algorithm has a significant improvement in accuracy of abnormal point detection.%为了降低原始数据中的勘误影响,提高数据质量,深入分析了常用的基于距离的异常点检测算法,提出了一种新的基于改进距离的异常点检测算法,舍去了传统算法中对DB(d,p)参数的设置.首先,为了解决终端的不确定性选择属性困难的问题,引入了"属性隶属度"的概念,简化了检测属性的选择方式;其次,为了解决由于数据分布不均匀而导致的检测准确率较低的问题,改进了常用的距离度量,并采用改进的加权距离进行计算,得到距离矩阵,通过分析计算距离的总值,给出了一种异常评价方法用来判断异常点的异常程度;最后,以股票交易数据进行实验,与传统基于距离和的检测算法进行比较,结果表明该改进算法在异常点检测的准确度方面具有明显的改善.
