Adaptive Spike Detection for Resilient Data Stream Mining

Conferences in Research and Practice in Information Technology Series

Volumn 70, Issue , 2007, Pages 181-188

  Phua, Clifton ^a   Smith Miles, Kate ^b   Lee, Vincent ^a   Gayler, Ross ^c  

^a MONASH UNIVERSITY   (Australia)

^b DEAKIN UNIVERSITY   (Australia)

^c Veda Advantage   (Australia)

Author keywords

Adaptive spike detection; Class label free attributes ranking and selection; Data stream mining; Resilient data mining

Indexed keywords

ATTRIBUTE RANKING; CREDIT APPLICATION; CRIME DETECTION; DATA STREAM MINING; DETECTION SYSTEM; F-MEASURE; RANKING AND SELECTION; RELATIVE WEIGHTS; SPIKE DETECTION;

DATA COMMUNICATION SYSTEMS; EXPERIMENTS;

DATA MINING;

EID: 84870477834     PISSN: 14451336     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Article

References (20)

1. Arasu, A., Babcock, B., Babu, S., Datar, M., Ito, K., Nishizawa, I., Rosenstein, J. & Widom, J. (2003), STREAM: the stanford stream data manager demonstration description, in 'SIGMOD03'.
2. Balakrishnan, H., Balazinska, M., Carney, D., Cetintemel, U., Cherniack, M., Convey, C., Galvez, E., Salz, J., Stonebraker, M., Tatbul, N., Tibbetts, R. & Zdonik, S. (2004), 'Retrospective on aurora', VLDB Journal 13(4), pp. 370-383.
3. Baxter, R., Christen, P. & Churches, T. (2003), A comparison of fast blocking methods for record linkage, in 'ACM SIGKDD03 Workshop on Data Cleaning, Record Linkage and Object Consolidation'.
4. Bolton, R. & Hand, D. (2001), Unsupervised profiling methods for fraud detection, in 'Credit Scoring and Credit Control VII'.
5. Christen, P. & Goiser, K. (2007), Quality and complexity measures for data linkage and deduplication, in F. Guillet & H. Hamilton, eds, 'Quality Measures in Data Mining', Springer.
6. Cortes, E., Pregibon, D. & Volinsky, C. (2003), 'Computational methods for dynamic graphs', Journal of Computational and Graphical Statistics 12, pp. 950-970.
7. Goldenberg, A., Shmueli, G. & Caruana, R. (2002), 'Using grocery sales data for the detection of bioterrorist attacks', Statistical Medicine.
8. Jackson, M., Baer, A., Painter, I. & Duchin, J. (2007), 'A simulation study comparing aberration detection algorithms for syndromic surveillance', BMC Medical Informatics and Decision Making 7(6).
9. Kleinberg, J. (2005), Temporal dynamics of on-line information streams, in M. Garofalakis, J. Gehrke & R. Rastogi, eds, 'Data Stream Management: Processing High-Speed Data Streams', Springer.
10. Kleinberg, J. (2002), Bursty and hierarchical structure in streams, in 'SIGKDD02'.
11. Phua, C., Lee, V., Smith-Miles, K. & Gayler, R. (2005), 'A comprehensive survey of data miningbased fraud detection research', Artificial Intelligence Review.
12. Phua, C., Lee, V., & Smith-Miles, K. (2006), 'The personal name problem and a recommended data mining solution', Encyclopedia of Data Warehousing and Mining (2nd Edition).
13. Phua, C., Lee, V., Gayler, R., & Smith-Miles, K. (2006a), Temporal representation in spike detection of sparse personal identity streams, in 'PAKDD06 Workshop on Intelligence and Security Informatics'.
14. Phua, C., Gayler, R., Smith-Miles, K. & Lee, V. (2006b), Communal detection of implicit personal identity streams, in 'IEEE ICDM06 Workshop on Mining Evolving and Streaming Data'.
15. Phua, C., Gayler, R., Lee, V. & Smith-Miles, K. (2006c), 'On the communal analysis suspicion scoring for identity crime in streaming credit applications', European Journal of Operational Research.
16. Phua, C., Lee, V., Smith-Miles, K. & Gayler, R. (2007), Adaptive communal detection in search of adversarial identity crime, in 'ACM SIGKDD07 Workshop on Domain-Driven Data Mining'.
17. Schneier, B. (2003), Beyond fear: thinking sensibly about security in an uncertain world, Copernicus, New York.
18. Winkler, W. (2006), 'Overview of record linkage and current research directions', Statistical Research Division, U.S. Census Bureau Publication, RR 2006-2.
19. Wong, W., Moore, A., Cooper, G. & Wagner, M. (2003), Bayesian network anomaly pattern detection for detecting disease outbreaks, in 'ICML03', pp. 217-223.
20. Yoshida, K., Adachi, F., Washio, T., Motoda, H., Homma, T., Nakashima, A., Fujikawa, H., & Yamazaki, K. (2004), Density-based spam detector, in 'SIGKDD04', pp. 486-493.