Applying topic modeling to forensic data

IFIP International Federation for Information Processing

Volumn 285, Issue , 2008, Pages 115-126

  de Waal, Alta ^a   Venter, Jacobus ^b   Barnard, Etienne ^a  

^a CSIR MERAKA INSTITUTE   (South Africa)

^b DSI CSIR Nanotechnology Innovation Centre   (South Africa)

Author keywords

Analysis phase; Digital investigation; Evidence mining; Topic modeling

Indexed keywords

ELECTRONIC CRIME COUNTERMEASURES; SEMANTICS; INFORMATION TECHNOLOGY;

ANALYSIS PHASE; ANALYSIS PROCESS; DIGITAL FORENSIC; DIGITAL INVESTIGATION; KNOWLEDGE DISCOVERY AND DATA MININGS; SEMANTIC CONTEXT; TOPIC MODELING; TOPIC MODELING ALGORITHMS;

DATA MINING; ELECTRONIC CRIME COUNTERMEASURES;

EID: 51149115983     PISSN: 15715736     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-0-387-84927-0_10     Document Type: Article

References (18)

1. N. Beebe and J. Clark, Digital forensic text string searching: Improving information retrieval effectiveness by thematically clustering search results, Digital Investigation, vol. 4S, pp. S49-S54, 2007.
2. D. Blei, A. Ng and M. Jordan, Latent Dirichlet allocation, Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003.
3. G. Botha, V. Zimu and E. Barnard, Text-based language identification for the South African languages, Proceedings of the Seventeenth Annual Symposium of the Pattern Recognition Association of South Africa, 2006.
4. E. Casey, Digital Evidence and Computer Crime, Academic Press, London, United Kingdom, 2000.
5. P. Chapman, J. Clinton, R. Kerber, T. Khabaza, T. Reinartzrysler, C. Shearer and R. Wirth, CRISP-DM 1.0: Step-by-Step Data Mining Guide, The CRISP-DM Consortium, SPSS, Chicago, Illinois (www.crisp-dm.org/ CRISPWP-0800.pdf), 1999.
6. T. Griffiths and M. Steyvers, Finding scientific topics, Proceedings of the National Academy of Sciences, vol. 101(1), pp 5228-5235, 2004.
7. T. Griffiths, M. Steyvers and J. Tenenbaum, Topics in semantic representation, Psychological Review, vol. 114(2), pp. 211-244, 2007.
8. D. Harman, Overview of the first text retrieval conference, Proceedings of the First Text Retrieval Conference, pp. 1-20, 1992.
9. A. Louis, A. de Waal and J. Venter, Named entity recognition in a South African context, Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists pp. 170-179, 2006.
10. D. Mackay, Information Theory, Inference and Learning Algorithms, Cambridge University Press, Cambridge, United Kingdom, 2003.
11. C. McCue, Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis, Butterworth-Heinemann, Burlington, Massachusetts, 2007.
12. Q. Mei and C. Zhai, Discovering evolutionary theme patterns from text: An exploration of temporal text mining, Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 198-207, 2005.
13. D. Newman, C. Chemudugunta, P. Smyth and M. Steyvers, Analyzing entities and topics in news articles using statistical topic models, Proceedings of the Intelligence and Security Informatics Conference, pp. 93-104, 2006.
14. M. Pollitt and A. Whitledge, Exploring big haystacks: Data mining and knowledge management, in Advances in Digital Forensics II, M. Olivier and S. Shenoi (Eds.), Springer, New York, pp. 67-76, 2006.
15. M. Porter, An algorithm for suffix stripping, Program, vol. 13(3), pp. 130-137, 1980.
16. L. Rigouste, O. Cappe and F. Yvon, Inference and evaluation of the multinomial mixture model for text clustering, Information Processing and Management, vol. 43(5), pp 1260-1280, 2007.
17. J. Venter, A. de Waal and N. Willers, Specializing CRISP-DM for evidence mining, in Advances in Digital Forensics III, P. Craiger and S. Shenoi (Eds.), Springer, New York, pp. 303-315, 2007.
18. X. Wang and A. McCallum, Topics over time: A non-Markov continuous-time model of topical trends, Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 424-433, 2006.