Approach based on wavelet analysis for detecting and amending anomalies in dataset

被引：1

作者：

彭小奇 ^{[1
]}

宋彦坡 ^{[1
]}

唐英 ^{[2
]}

张建智 ^{[1
]}

机构：

[1] School of Energy Science and Engineering, Central South University

[2] School of Physics Science and Technology, Central South University

来源：

Journal of Central South University of Technology(English Edition) | 2006年 / 05期

基金：

中国国家自然科学基金;

关键词：

data preprocessing; wavelet analysis; anomaly detecting; data mining;

D O I：

暂无

中图分类号：

TP311.13 [];

学科分类号：

1201 ;

摘要：

It is difficult to detect the anomalies whose matching relationship among some data attributes is very different from others’ in a dataset. Aiming at this problem, an approach based on wavelet analysis for detecting and amending anomalous samples was proposed. Taking full advantage of wavelet analysis’ properties of multi-resolution and local analysis, this approach is able to detect and amend anomalous samples effectively. To realize the rapid numeric computation of wavelet translation for a discrete sequence, a modified algorithm based on Newton-Cores formula was also proposed. The experimental result shows that the approach is feasible with good result and good practicality.

引用

页码：491 / 495

页数：5

共 3 条

[1]

Two-phase clustering process for outliers detection[J] . M.F Jiang,S.S Tseng,C.M Su.Pattern Recognition Letters . 2001 (6)

[2]

Finding intentional knowledge of distance-based outliers. Knorr E M, Ng R T. Proceedings of the 25th International Conference on Very Large Data Bases . 1999

[3]

On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms[C. Yamanishi K,Takeuchi J I,Williams G. Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 2000

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