[1]张洁玲.一种基于近邻关系的新型离群评估算法[J].福建工程学院学报,2017,15(06):591-596.[doi:10.3969/j.issn.1672-4348.2017.06.017]
　Zhang Jieling.A new outlier evaluation algorithm based on the nearest neighbor relationship[J].Journal of FuJian University of Technology,2017,15(06):591-596.[doi:10.3969/j.issn.1672-4348.2017.06.017]
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一种基于近邻关系的新型离群评估算法()
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《福建工程学院学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第15卷
期数:
2017年06期
页码:
591-596
栏目:
出版日期:
2017-12-25

文章信息/Info

Title:
A new outlier evaluation algorithm based on the nearest neighbor relationship
作者:
张洁玲
福建江夏学院电子信息科学学院
Author(s):
Zhang Jieling
School of Electrical and Information Science, Fujian Jiangxia University
关键词:
CDD算法;  k-近邻;  离群评估
Keywords:
CDD algorithm;  k-nearest neighbor;  outlier evaluation
分类号:
TP301.6
DOI:
10.3969/j.issn.1672-4348.2017.06.017
文献标志码:
A
摘要:
针对传统离群点检测算法的局限性进行研究，利用数据对象之间的相邻关系，提出了一种基于密度和距离相结合的离群检测算法，该算法解决了基于距离的离群检测算法不能准确识别局部离群点的问题，有效避免由于稀疏和密集簇过于邻近的而出现离群点误判的情况。通过在人工模拟数据及真实数据集上的实验测试证明改进算法的可行性，该算法能更有效地检测出数据集中的离群对象。
Abstract:
Aiming at the limitations of traditional outliers detection algorithm, a new outlier detection algorithm based on the combination of density and distance was proposed according to the neighboring relationships of the data. The new algorithm solves the problem that the distance-based algorithm cannot identify local outliers, and effectively avoids wrong detection of outliers when the sparse clusters and dense clusters are too close. Experiments on artificial and real datasets prove that the improved algorithm is feasible and it can detect the outliers in the datasets more effectively.

参考文献/References:

[1] He Zengyou， Xu Xiaofei， Deng Shengchun． Discovering cluster-based local outliers[J]． Pattern Recognition Letters，2003，24(9)：1641-1650．
[2] Breunig M， Kriegel H P， Ng R， et a1． LOF：Identilying densityhased local outliers[C]∥. Proc of the ACM SIGMOD International Conference on Management of Data，2000：93-104．
[3] Papadimitrious S， Kitawaga H， Gibbons P B， et al． LOCI： Fast outlier detection using the local correlation integral[C]∥. Proc of the 19th International Conference on Data Engineering，2002：315-326．
[4] Tang J， Chen Z， Fu A， et al. Enhancing effectiveness of outlier detections for low-density pattern[J]． Proceedings of the 6th PAKDD，2002,2236:535-548．
[5] 王敬华，赵新想，张国燕，等．NLOF：一种新的基于密度的局部离群点检测算法[J]．计算机科学，2013，40(8)：181-185．
[6] Ramaswamy S， Rastogi R， Kyuseok S． Efficient algorithms for mining outliers from large data sets[C]∥. Proc of the ACM SIGMOD International Conference on Management of Data，2000：427-438．
[7] Angiulli F， Pizzuti C． Outlier mining in large high-dimensional data sets[J]． IEEE Trans. Knowledge and Data Eng，2005，2(17)：203-215．
[8] 范小刚，朱庆生，万家强．基于K-近邻树的离群检测算法[J]．计算机应用研究，2015，32(3)：669-673．
[9] 周世波，徐维祥．一种基于偏离的局部离群点检测算法[J]．仪器仪表学报，2014，35（10）： 2293-2297.

更新日期/Last Update: 2017-12-25