An efficient outlier detection method for data streams based on closed frequent patterns by considering anti-monotonic constraints
An efficient outlier detection method for data streams based on closed frequent patterns by considering anti-monotonic constraints
复制标题
考虑反单调约束的基于闭合频繁模式的数据流高效离群点检测方法
DOI：
10.1016/j.ins.2020.12.050
复制
发表时间：
2021-01-09
影响因子：
8.1
通讯作者：
Geng, Ye
中科院分区：
文献类型：
--
作者：
Cai, Saihua;Huang, Rubing;Geng, Ye
在一些实际应用中，用户更关注的是自己感兴趣的一小部分数据，而不是完整的数据元素集合，因此提高这一小部分数据的安全性就显得极为重要。现有的基于关联度的离群点检测方法都是从完整的数据流集合中检测出离群点，导致用户需要等待很长时间才能得到检测结果。相反，大多数离群值的分布对用户来说是没有意义的。针对这一问题，通过考虑用户指定的反单调约束，提出了一种基于闭合频繁模式的数据流离群点检测方法。同时，设计了四个偏离指标，通过考虑更多的影响因素，准确计算滑动窗口中每笔交易的离群值。然后，具有最大离群值分数的前k个交易作为离群值返回。大量的实验结果表明，该方法能够以较少的时间开销准确地从满足反单调约束的数据流中发现离群点。(C)2020爱思唯尔公司All rights reserved.
In some practical applications, users pay more attention to a small part of the data that they are interested in, rather than full sets of the data elements, so it is extremely important to improve the security of this small part of the data. For the existing association-based outlier detection methods, they detect the outliers from full set of the data streams, which results in having users to wait for a long period to obtain the detection results. In contrast, most distributions of the outliers are meaningless for the users. To solve this problem, by considering user-specified anti-monotonic constraints, this paper proposes an efficient outlier detection method based on closed frequent patterns for data streams. Also, four deviation indices are designed to accurately calculate the outlier score of each transaction in the sliding window by considering more influencing factors. Then, the top k transactions with the largest outlier score are returned as outliers. Extensive experimental results show that the proposed method can accurately find the outliers from the data streams that satisfy the anti-monotonic constraints with less time cost. (C) 2020 Elsevier Inc. All rights reserved.