Temporal representation in spike detection of sparse personal identity streams

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3917 LNCS, Issue , 2006, Pages 115-126

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

^a MONASH UNIVERSITY   (Australia)

^b Baycorp Advantage   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

PERSONAL IDENTITY STREAMS; SPIKE DETECTION; TEMPORAL REPRESENTATION;

COMPUTER SOFTWARE; FORMAL LOGIC; NUMERICAL METHODS;

KNOWLEDGE REPRESENTATION;

EID: 33745803865     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11734628_14     Document Type: Conference Paper

References (16)

1. Arasu, A., Babcock, B., Babu, S., Datar, M., Ito, K., Nishizawa, I., Rosenstein, J. and Widom, J.: STREAM: The Stanford Stream Data Manager Demonstration description -short overview of system status and plans. Proceedings of SIGMOD03, 2003.
2. Balakrishnan, H., Balazinska, M., Carney, D., Çetintemel, U., Cherniack, M., Convey, C., Galvez, E., Salz, J., Stonebraker, M., Tatbul, N., Tibbetts, R., Zdonik, S.: Retrospective on Aurora. VLDB Journal, 13(4), 2004, pp370-383.
3. Caruana, R. and Niculescu-Mizil, A.: Data Mining in Metric Space: An Empirical Analysis of Supervised Learning Performance Criteria. Proceedings of SIGKDD04, 2004, pp69-78.
4. Christen, P.: Probabilistic Data Generation for Deduplication and Data Linkage. Proceedings of IDEAL05, 2005.
5. Cook, M.: Fraud and ID Theft - The Lowdown on Fraud Rings. In Collections and Credit Risk 10, 2005.
6. Fawcett, T. and Provost, F.: Activity Monitoring: Noticing Interesting Changes in Behaviour. Proceedings of SIGKDD99, 1999, pp53-62.
7. Goldenberg, A., Shmueli, G. and Caruana, R.: Using Grocery Sales Data for the Detection of Bio-Terrorist Attacks. In Statistical Medicine, Submitted, 2002.
8. Keogh, E., Chu, S., Hart, D. and Pazzani, M.: Segmenting Time Series: A Survey and Novel Approach. In Last, M., Kandel, A. and Horst, B. (eds.): Data Mining in Time Series Databases, World Scientific, 2004.
9. Kleinberg, J.: Bursty and Hierarchical Structure in Streams. Proceedings of SIGKDD02, 2002, pp91-101.
10. Kleinberg, J.: Temporal Dynamics of On-Line Information Streams. In Garofalakis, M., Gehrke, J. and Rastogi, R. (eds.): Data Stream Management: Processing High-Speed Data Streams, Springer, 2005.
11. th edition.
12. Percival, D. and Walden, A.: Wavelet Methods for Time Series Analysis (WMTSA). Cambridge University Press, 2000.
13. Phua, C., Lee, V., Gayler, R. and Smith, K.: A Comprehensive Survey of Data Mining-based Fraud Detection Research. Artificial Intelligence Review, submitted.
14. Phua, C., Gayler, R., Lee, V. and Smith, K.: On the Approximate Communal Fraud Scoring of Credit Applications. Proceedings of Credit Scoring and Credit Control, 2005.
15. Roberts, S.: Control-Charts-Tests based on Geometric Moving Averages. In Technometrics, 1, pp239-250.
16. Wong, W.: Data Mining for Early Disease Outbreak Detection. PhD Thesis, Carnegie Mellon University, 2004.