Fast mining of distance-based outliers in high-dimensional datasets

Data Mining and Knowledge Discovery

Volumn 16, Issue 3, 2008, Pages 349-364

  Ghoting, Amol ^a   Parthasarathy, Srinivasan ^b   Otey, Matthew Eric ^c  

^a IBM T J WATSON RESEARCH CENTER   (United States)

^b OHIO STATE UNIVERSITY   (United States)

^c GOOGLE INC   (United States)

Author keywords

Approximate k nearest neighbors; Clustering; High dimensional datasets; Outlier detection

Indexed keywords

CLUSTERING ALGORITHMS; EDGE DETECTION; NUMBER THEORY;

APPROXIMATE K-NEAREST NEIGHBORS; CLUSTERING; HIGH-DIMENSIONAL DATASETS; OUTLIER DETECTION;

DATA MINING;

EID: 42749086305     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-008-0093-2     Document Type: Article

References (23)

1. Angiulli F, Pizzuti C (2002) Fast outlier detection in high dimensional spaces. In: Proceedings of the international conference on principles of data mining and knowledge discovery
2. Arya S, Mount DM, Netanyahu NS, Silverman R and Wu AY (1998). An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J ACM 45(6): 891-923
3. Barnett V, Lewis T (1994) Outliers in statistical data. John Wiley and Sons
4. Bay S (1999). The UCI KDD archive. University of California, Department of Information and Computer Science, Irvine, CA
5. Bay S, Schwabacher M (2003) Mining distance-based outliers in near linear time with randomization and a simple pruning rule. In: Proceedings of the international conference on knowledge discovery and data mining
6. Bentley J (1975). Multidimensional binary search trees used for associative searching. Commun ACM 18: 509-517
7. Berchtold S, Keim D, Kreigel H (1996) The X-tree: an index structure for high dimensional data. In: Proceedings of the international conference on very large data bases (VLDB)
8. Bolton R and Hand D (2002). Statistical fraud detection: a review. Stat Sci 17: 235-255
9. Gamberger D, Lavrac N, Groselj C (1999) Experiments with noise filtering in the medical domain. In: Proceedings of the international conference on machine learning
10. Ghoting A, Parthasarathy S, Otey M (2005) Fast mining of distance-based outliers in high dimensional datasets. Technical report TR71, Department of Computer Science and Engineering, The Ohio State University
11. Guttmann R (1984) A dynamic index structure for spatial searching. In: Proceedings of the international conference on management of data (SIGMOD)
12. Hartigan J (1975) Clustering algorithms. John Wiley and Sons
13. Indyk P, Motwani R (1998) Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the symposium on theory of computing (STOC), pp 604-613
14. Jagadish H, Poosala V, Koudas N, Sevcik K, Muthukrishnan S, Suel T (1998) Optimal histograms with guarantees. In: Proceedings of the international conference on very large databases (VLDB)
15. Jolliffe I (1986) Principal component analysis. Springer-Verlag
16. Kleinberg JM (1997) Two algorithms for nearest-neighbor search in high dimensions. In: Proceedings of the symposium on theory of computing (STOC), pp 599-608
17. Knorr E, Ng R (1999) Finding intensional knowledge of distance-based outliers. In: Proceedings of the international conference on very large data bases (VLDB)
18. Kushilevitz E, Ostrovsky R, Rabani Y (1998) Efficient search for approximate nearest neighbor in high dimensional spaces. In: Proceedings of the symposium on theory of computing (STOC)
19. Muralikrishna M, DeWitt D (1988) Equi-depth histograms for estimating selectivity factors for multidimensional queries. In: Proceedings of the international conference on management of data (SIGMOD)
20. Ramaswamy S, Rastogi R, Shim K (2000) Efficient algorithms for mining outliers from large datasets. In: Proceedings of the international conference on management of data
21. Ruggles S, Sobek M (1997) Integrated public use microdata series: version 2.0
22. Sequeira K, Zaki M (2002) ADMIT: anomaly-based data mining for intrusions. In: Proceedings of the international conference on knowledge discovery and data mining
23. Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. In: Proceedings of the international conference on management of data (SIGMOD)