Detection and prediction of distance-based outliers

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Detection and prediction of distance-based outliers

Full text

Pdf (11.41 MB)

Source

Symposium on Applied Computing archive
Proceedings of the 2005 ACM symposium on Applied computing table of contents

Santa Fe, New Mexico

SESSION: Data mining (DM) table of contents

Pages: 537 - 542

Year of Publication: 2005

ISBN:1-58113-964-0

Authors

Fabrizio Angiulli	ICAR-CNR, Via Pietro Bucci, Rende (CS), Italy
Stefano Basta	ICAR-CNR, Via Pietro Bucci, Rende (CS), Italy
Clara Pizzuti	ICAR-CNR, Via Pietro Bucci, Rende (CS), Italy

Sponsor

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 10, Downloads (12 Months): 69, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTex EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1066677.1066797 What is a DOI?

ABSTRACT

In this paper we present an unsupervised distance-based outlier detection method designed to learn a model over the objects contained in a data set. The learned model, called solving set, is a small subset of the data set that is used to classify new unseen objects as outliers or not. We provide an algorithm that computes a solving set with sub-quadratic time requirements, and we give experimental evidence that the computed solving set is small and that the false positive rate, i.e. the fraction of new objects misclassified as outliers using the solving set instead of the overall data set, is negligible.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Fabrizio Angiulli , Clara Pizzuti, Fast Outlier Detection in High Dimensional Spaces, Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, p.15-26, August 19-23, 2002
	2	A. Arning, C. Aggarwal, and P. Raghavan. A linear method for deviation detection in large databases. In Proc. Int. Conf. on Knowledge Discovery and Data Mining(KDD'96), pages 164--169, 1996.
	3	V. Barnett and T. Lewis. Outliers in Statistical Data. John Wiley & Sons, 1994.
	4	Stephen D. Bay , Mark Schwabacher, Mining distance-based outliers in near linear time with randomization and a simple pruning rule, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C. [doi>10.1145/956750.956758]
	5	Markus M. Breunig , Hans-Peter Kriegel , Raymond T. Ng , Jörg Sander, LOF: identifying density-based local outliers, Proceedings of the 2000 ACM SIGMOD international conference on Management of data, p.93-104, May 15-18, 2000, Dallas, Texas, United States
	6	E. Eskin, A. Arnold, M. Prerau, L. Portnoy, and S. Stolfo. A geometric framework for unsupervised anomaly detection. In Applications of Data Mining in Computer Security, Kluwer, 2002.
	7	Tom Fawcett , Foster Provost, Adaptive Fraud Detection, Data Mining and Knowledge Discovery, v.1 n.3, p.291-316, 1997 [doi>10.1023/A:1009700419189 ]
	8	Sally Floyd , Manfred Warmuth, Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension, Machine Learning, v.21 n.3, p.269-304, Dec. 1995 [doi>10.1023/A:1022660318680]
	9	Jiawei Han , Micheline Kamber, Data mining: concepts and techniques, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2000
	10	Wen Jin , Anthony K. H. Tung , Jiawei Han, Mining top-n local outliers in large databases, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.293-298, August 26-29, 2001, San Francisco, California [doi>10.1145/502512.502554]
	11	Edwin M. Knorr , Raymond T. Ng, Algorithms for Mining Distance-Based Outliers in Large Datasets, Proceedings of the 24rd International Conference on Very Large Data Bases, p.392-403, August 24-27, 1998
	12	Edwin M. Knorr , Raymond T. Ng , Vladimir Tucakov, Distance-based outliers: algorithms and applications, The VLDB Journal — The International Journal on Very Large Data Bases, v.8 n.3-4, p.237-253, February 2000 [doi>10.1007/s007780050006]
	13	A. Lazarevic, L. Ertoz, V. Kumar, A. Ozgur, and J. Srivastava. A comparative study of anomaly detection schemes in network intrusion detection. In Proc. SIAM Int. Conf. on Data Mining, 2003.
	14	W. Lee, S. J. Stolfo, and K. W. Mok. Mining audit data to build intrusion detection models. In Proc. Int. Conf on Knowledge Discovery and Data Mining (KDD-98), pages 66--72, 1998.
	15	Sridhar Ramaswamy , Rajeev Rastogi , Kyuseok Shim, Efficient algorithms for mining outliers from large data sets, Proceedings of the 2000 ACM SIGMOD international conference on Management of data, p.427-438, May 15-18, 2000, Dallas, Texas, United States
	16	Kenji Yamanishi , Jun-ichi Takeuchi, Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learner, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.389-394, August 26-29, 2001, San Francisco, California [doi>10.1145/502512.502570]