Resolution-based outlier factor: Detecting the top-n most outlying data points in engineering data

Knowledge and Information Systems

Volumn 19, Issue 1, 2009, Pages 31-51

  Fan, Hongqin ^a   Zaïane, Osmar R ^b   Foss, Andrew ^b   Wu, Junfeng ^b  

^a MISSOURI WESTERN STATE UNIVERSITY   (United States)

^b UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ANOMALY DETECTION; CONSTRUCTION EQUIPMENT; DATA HANDLING; DECISION SUPPORT SYSTEMS; LARGE DATASET; STATISTICS;

CLUSTER FEATURE; DATA REPOSITORIES; DOMAIN PARAMETERS; ENGINEERING APPLICATIONS; ENGINEERING DATA; LARGE AMOUNTS OF DATA; SYNTHETIC DATASETS; VIABLE SOLUTIONS;

DATA MINING;

EID: 63649164988     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-008-0145-3     Document Type: Article

References (24)

1. Breunig M, Kriegel H, Ng R, Sander J (2000) LOF: Identifying density-based local outliers. In: Proceedings of ACM SIGMOD international conference on management of data, Dallas
2. Ester M, Kriegel H, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of conference on knowledge discovery and data mining (KDD), Portland, Oregon, USA
3. Fisher D, Xu L, Carmes JR, Chen J, Shiavi R, Biswas G, Weinberg J (1993) Applying AI clustering to engineering tasks. IEEE Intell Syst 8(6):51-60
4. Foss A, Zaïane O (2002) A parameterless method for efficiently discovering clusters of arbitrary shape in large datasets. In: Proceedings of 2002 IEEE international conference on data mining (ICDM'02), Maebashi City, Japan
5. Gnanadesikan R, Kettenring JR (1972) Robust estimates, residuals, and outlier detection with multi-response data. Biomet J Int Biomet Soc 28:81-124
6. Goldstein J, Ramakrishnan R (2000) Contrast plots and P-sphere trees: space vs. time in nearest neighbor searches. In: Proceedings of 26th conference on very large databases (VLDB), pp 429-440
7. Hawkins D (1980) Identification of outliers. Chapman and Hall, London
8. Howell DA, Shenton HW (2005) A System for in-service strain monitoring of ordinary bridges. In: Proceedings of the 2005 structures congress and forensic engineering symposium, New York, NY, USA
9. Indyk P, Motwani R (1998) Approximate nearest neighbors: Towards removing the curse of dimensionality. In: Proceedings of STOC, pp 604-613
10. Jin W, Tung AKH, Han JW (2001) Mining top-n local outliers in large databases. In: Proceedings of conference on knowledge discovery and data mining (KDD), San Francisco, CA, USA
11. Kantardzic M (2003) Data mining: Concepts, models, methods, and algorithms. Wiley, New York
12. Karypis G, Han E, Kumar V (1999) Chameleon: Hierarchical clustering using dynamic modeling. IEEE Comput 32(8):68-75. doi: 10.1109/2.781637
13. Knorr E, Ng R (1998) Algorithms for mining distance-based outliers in large datasets. In: Proceedings of 24th international conference on very large databases (VLDB), New York, USA
14. Kushilevitz E, Ostrovsky R, Rabani Y (1998) Efficient search for approximate nearest neighbor in high dimensional spaces. In: Proceedings of STOC
15. Liu T, Moore AW, Gray A, Yang K (2004) An Investigation of practical approximate nearest neighbour algorithms. NIPS, December
16. Ng RT, Han J (1994) Efficient and effective clustering methods for spatial data mining. In: Proceedings of 20th conference on very large databases (VLDB), Santiago, Chile, pp 144-155
17. Norbert B, Kriegel H-P, Schneider R, Seeger B (1990) The R*-Tree: an efficient and robust access method for points and rectangles. In: Proceedings of the ACM SIGMOD international conference on management of data, pp 322-331
18. Patak Z (1990) Robust principal component analysis via project pursuit. MSc Thesis, University of British Columbia, Canada
19. Pena D, Prieto F (2001) Multivariate outlier detection and robust covariance matrix estimation. Technometrics, American Statistical Association and the American Society for Quality, vol 43, no. 3
20. Ramaswamy S, Rastogi R, Shim K (2000) Efficient algorithms for mining outliers from large data sets. In: Proceedings of the ACM SIGMOD international conference on management of data, Dallas, TX, USA
21. Raz O, Buchheit R, Shaw M, Koopman P, Faloutsos C (2004) Detecting Semantic anomalies in truck weigh-in-motion traffic data using data mining. J Comput Civil Eng ASCE 18(4):291-300
22. Tang J, Chen Z, Fu AW, Cheung DW (2002) Enhancing effectiveness of outlier detections for low density patterns. In: Proceedings of the 6th Pacific-Asia conference on advances in knowledge discovery and data mining, Taipei, Taiwan, pp 535-548
23. Tang J, Chen Z, Fu AW, Cheung DW (2006) Capabilities of outlier detection schemes in large datasets, framework and methodologies. Knowl Inf Syst 00:1-41
24. Toru K, Katsuya I, Satoru F, Hong J, Hiroshi K (1997) Equipment monitoring system. Yokogawa technical report english edition. No.24, Yokogawa Electric Corporation