2009 International Conference on Information Technology and Computer Science 2009
DOI: 10.1109/itcs.2009.230
View full text |Buy / Rent full text
|
Sign up to set email alerts
|

Abstract: Outlier detection has received significant attention in many applications, such as detecting credit card fraud or network intrusions. Distance-based outlier detection is an important data mining technique that finds abnormal data objects according to some distance function. However, when this technique is applied to datasets whose density distribution is different, usually the detection efficiency and result are not perfect. With analysis of features of outliers in datasets, as the improvement of Local Sparsit… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
4
0

Year Published

2013
2013
2020
2020

Publication Types

Select...
3

Relationship

0
3

Authors

Journals

citations
Cited by 10 publications
(21 citation statements)
references
References 18 publications
(21 reference statements)
0
4
0
Order By: Relevance
“…In this paper this model has been applied on Breast Cancer, Nursery data and Bank marketing data from UCI Machine repository [9]. This method has implemented the approach of using MATLAB tool.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…In this paper this model has been applied on Breast Cancer, Nursery data and Bank marketing data from UCI Machine repository [9]. This method has implemented the approach of using MATLAB tool.…”
Section: Resultsmentioning
confidence: 99%
“…Similarly one record is selected from each eight records and ten records and repeated the same process. This method has been implemented on Nursery dataset, Breast cancer and Bank dataset which are taken from UCI Machine learning repository [9]. This method compared with different number of outliers from each sample.…”
Section: Resultsmentioning
confidence: 99%
“…In the literature, two approaches to acquire annotated outlier data are utilized: either generate data with outliers [4,33,78] or sample imbalanced data from existing datasets [51,82]. We utilize the second option, because many outlier publications sample imbalanced data [33,34,41,42,69,78,79] to validate outlier detection.…”
Section: Datamentioning
confidence: 99%
“…Yu et al [41] proposed the local isolation coefficient (LIC) to score the outlierness of an object. The LIC of object p, LIC(p), is defined in Eq.…”
Section: Distance-based Approachesmentioning
confidence: 99%