Article

Outlier Detection Integrating Semantic Knowledge

Authors:

Zengyou He,

Shengchun Deng,

Xiaofei XuAuthors Info & Claims

WAIM '02: Proceedings of the Third International Conference on Advances in Web-Age Information Management

Pages 126 - 131

Published: 11 August 2002 Publication History

Abstract

Existing proposals on outlier detection didn't take the semantic knowledge of the dataset into consideration. They only tried to find outliers from dataset itself, which prevents finding more meaningful outliers. In this paper, we consider the problem of outlier detection integrating semantic knowledge. We introduce new definition for outlier: semantic outlier. A semantic outlier is a data point, which behaves differently with other data points in the same class. A measure for identifying the degree of each object being an outlier is presented, which is called semantic outlier factor (SOF). An efficient algorithm for mining semantic outliers based on SOF is also proposed. Experimental results show that meaningful and interesting outliers can be found with our method.

References

[1]

E. M. Knorr, R. T. Ng: Algorithms for Mining Distance-Based Outliers in Large Datasets. Proc. 24th Int. Conf. on Very Large Database, New York, NY, 1998, pp. 392-403.

Crossref

Google Scholar

[2]

S. Ramaswamy, R. Rastogi, S. Kyuseok: Efficient Algorithms for Mining Outliers from Large Data Sets. Proc. ACM SIGMOD 2000 Int. Conf. on Management of Data , Dallas, Texas, 2000.

Crossref

Google Scholar

[3]

M. M. Breunig, H. P. Kriegel, R. T. Ng, J. Sander: LOF: Identifying Density-Based Local Outliers". Proc. ACM SIGMOD 2000 Int. Conf. on Management of Data, Dallas, Texas, 2000.

Crossref

Google Scholar

[4]

C. Aggarwal, P. Yu: Outlier Detection for High Dimensional Data. Proc. of the 2001 ACM SIGMOD Int'1 Conf. Management of Data, pp. 37-46, Santa Barbara, CA, USA.

Crossref

Google Scholar

[5]

Z. He, S. Deng and X. Xu: Squeezer : An Efficient Algorithm for Clustering Categorical Data. Technical Report, HIT, 2001. http://202.118.239.67/tech/squeezer.pdf. To appear in Journal of Computer Science and Technology.

Google Scholar

[6]

C. J. Merz, Murphy: UCI Repository of Machine Learning Databases. (Http://www.ics.uci.edu/~mlearn/MLRRepository.html).

Google Scholar

Cited By

View all

Ahmad AShaari FZalizah AKim DKim SLee SHanzo LIsmail R(2015)Mining meaningful outlier using rough-negative association algorithm in heartdisease datasetProceedings of the 9th International Conference on Ubiquitous Information Management and Communication10.1145/2701126.2701182(1-7)Online publication date: 8-Jan-2015
https://dl.acm.org/doi/10.1145/2701126.2701182
Chen PWong LLi J(2012)Detection of Outlier Residues for Improving Interface Prediction in Protein HeterocomplexesIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2012.589:4(1155-1165)Online publication date: 1-Jul-2012
https://dl.acm.org/doi/10.1109/TCBB.2012.58
Chandola VBanerjee AKumar V(2009)Anomaly detectionACM Computing Surveys10.1145/1541880.154188241:3(1-58)Online publication date: 30-Jul-2009
https://dl.acm.org/doi/10.1145/1541880.1541882
Show More Cited By

Outlier Detection Integrating Semantic Knowledge
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
2. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

Enhancing Outlier Detection by an Outlier Indicator
Machine Learning and Data Mining in Pattern Recognition
Abstract
Outlier detection is an important task in data mining and has high practical value in numerous applications such as astronomical observation, text detection, fraud detection and so on. At present, a large number of popular outlier detection ...
Outlier detection using isolation forest and local outlier factor
RACS '19: Proceedings of the Conference on Research in Adaptive and Convergent Systems

Outlier detection, also named as anomaly detection, is one of the hot issues in the field of data mining. As well-known outlier detection algorithms, Isolation Forest(iForest) and Local Outlier Factor(LOF) have been widely used. However, iForest is only ...
Outlier detection based on rough sets theory

An outlier in a dataset is a point or a class of points that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of outliers is important for many applications and has always attracted attention among data mining ...

Comments

Information & Contributors

Information

Published In

WAIM '02: Proceedings of the Third International Conference on Advances in Web-Age Information Management

August 2002

443 pages

ISBN:3540440453

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 11 August 2002

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Ahmad AShaari FZalizah AKim DKim SLee SHanzo LIsmail R(2015)Mining meaningful outlier using rough-negative association algorithm in heartdisease datasetProceedings of the 9th International Conference on Ubiquitous Information Management and Communication10.1145/2701126.2701182(1-7)Online publication date: 8-Jan-2015
https://dl.acm.org/doi/10.1145/2701126.2701182
Chen PWong LLi J(2012)Detection of Outlier Residues for Improving Interface Prediction in Protein HeterocomplexesIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2012.589:4(1155-1165)Online publication date: 1-Jul-2012
https://dl.acm.org/doi/10.1109/TCBB.2012.58
Chandola VBanerjee AKumar V(2009)Anomaly detectionACM Computing Surveys10.1145/1541880.154188241:3(1-58)Online publication date: 30-Jul-2009
https://dl.acm.org/doi/10.1145/1541880.1541882
Yanpo SYing TXiaoqi PWen WLu T(2006)An approach based on wavelet analysis and non-linear mapping to detect anomalies in datasetProceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery10.1007/11881599_64(545-548)Online publication date: 24-Sep-2006
https://dl.acm.org/doi/10.1007/11881599_64
He ZDeng SXu XHuang J(2006)A fast greedy algorithm for outlier miningProceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining10.1007/11731139_67(567-576)Online publication date: 9-Apr-2006
https://dl.acm.org/doi/10.1007/11731139_67
He ZDeng SXu X(2005)An optimization model for outlier detection in categorical dataProceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I10.1007/11538059_42(400-409)Online publication date: 23-Aug-2005
https://dl.acm.org/doi/10.1007/11538059_42
Janeja VAtluri VVaidya JAdam N(2005)Collusion set detection through outlier discoveryProceedings of the 2005 IEEE international conference on Intelligence and Security Informatics10.1007/11427995_1(1-13)Online publication date: 19-May-2005
https://dl.acm.org/doi/10.1007/11427995_1
He ZXu XDeng S(2003)Discovering cluster-based local outliersPattern Recognition Letters10.1016/S0167-8655(03)00003-524:9-10(1641-1650)Online publication date: 1-Jun-2003
https://dl.acm.org/doi/10.1016/S0167-8655%2803%2900003-5

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

References

Cited By

Recommendations

Enhancing Outlier Detection by an Outlier Indicator

Outlier detection using isolation forest and local outlier factor

Outlier detection based on rough sets theory

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Get Access

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations