Document clustering for forensic analysis: An approach for improving computer inspection

IEEE Transactions on Information Forensics and Security

Volumn 8, Issue 1, 2013, Pages 46-54

  Da Cruz Nassif, Luís Filipe ^a,b   Hruschka, Eduardo Raul ^c,d  

^a Brazilian Federal Police Department   (Brazil)

^b UNIVERSITY OF BRASILIA   (Brazil)

^c UNIVERSITY OF SÃO PAULO   (Brazil)

^d UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

Clustering; forensic computing; text mining

Indexed keywords

AUTOMATED METHODS; CLUSTERING; CLUSTERING DOCUMENTS; COMPUTER INSPECTION; DOCUMENT CLUSTERING; FORENSIC ANALYSIS; FORENSIC COMPUTING; K-MEANS; K-MEDOIDS; NUMBER OF CLUSTERS; POLICE INVESTIGATIONS; REAL-WORLD DATASETS; SINGLE LINK; TEXT MINING; VALIDITY INDEX;

COMPUTER FORENSICS; DATA MINING; EXPERIMENTS; INFORMATION RETRIEVAL; NATURAL LANGUAGE PROCESSING SYSTEMS;

CLUSTERING ALGORITHMS;

EID: 84872043337     PISSN: 15566013     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIFS.2012.2223679     Document Type: Article

References (29)

1. J. F. Gantz, D. Reinsel, C. Chute, W. Schlichting, J. McArthur, S. Minton, I. Xheneti, A. Toncheva, and A. Manfrediz, "The expanding digital universe: A forecast of worldwide information growth through 2010," Inf. Data, vol. 1, pp. 1-21, 2007.
2. B. S. Everitt, S. Landau, and M. Leese, Cluster Analysis. London, U.K.: Arnold, 2001.
3. A. K. Jain and R. C. Dubes, Algorithms for Clustering Data. Englewood Cliffs, NJ: Prentice-Hall, 1988.
4. L. Kaufman and P. Rousseeuw, Finding Groups in Gata: An Introduction to Cluster Analysis. Hoboken, NJ: Wiley-Interscience, 1990.
5. R. Xu and D. C.Wunsch, II, Clustering. Hoboken, NJ: Wiley/IEEE Press, 2009.
6. A. Strehl and J. Ghosh, "Cluster ensembles: A knowledge reuse framework for combining multiple partitions," J. Mach. Learning Res., vol. 3, pp. 583-617, 2002.
7. E. R. Hruschka, R. J. G. B. Campello, and L. N. de Castro, "Evolving clusters in gene-expression data," Inf. Sci., vol. 176, pp. 1898-1927, 2006. (Pubitemid 44321891)
8. B. K. L. Fei, J. H. P. Eloff, H. S. Venter, andM. S. Oliver, "Exploring forensic data with self-organizing maps," in Proc. IFIP Int. Conf. Digital Forensics, 2005, pp. 113-123.
9. N. L. Beebe and J. G. Clark, "Digital forensic text string searching: Improving information retrieval effectiveness by thematically clustering search results," Digital Investigation, Elsevier, vol. 4, no. 1, pp. 49-54, 2007. (Pubitemid 47081439)
10. R. Hadjidj, M. Debbabi, H. Lounis, F. Iqbal, A. Szporer, and D. Benredjem, "Towards an integrated e-mail forensic analysis framework," Digital Investigation, Elsevier, vol. 5, no. 3-4, pp. 124-137, 2009.
11. F. Iqbal, H. Binsalleeh, B. C. M. Fung, and M. Debbabi, "Mining writeprints from anonymous e-mails for forensic investigation," Digital Investigation, Elsevier, vol. 7, no. 1-2, pp. 56-64, 2010.
12. S. Decherchi, S. Tacconi, J. Redi, A. Leoncini, F. Sangiacomo, and R. Zunino, "Text clustering for digital forensics analysis," Computat. Intell. Security Inf. Syst., vol. 63, pp. 29-36, 2009.
13. K. Stoffel, P. Cotofrei, and D. Han, "Fuzzy methods for forensic data analysis," in Proc. IEEE Int. Conf. Soft Computing and Pattern Recognition, 2010, pp. 23-28.
14. L. Vendramin, R. J. G. B. Campello, and E. R. Hruschka, "Relative clustering validity criteria: A comparative overview," Statist. Anal. Data Mining, vol. 3, pp. 209-235, 2010.
15. G. Salton and C. Buckley, "Term weighting approaches in automatic text retrieval," Inf. Process. Manage., vol. 24, no. 5, pp. 513-523, 1988.
16. L. Liu, J. Kang, J. Yu, and Z. Wang, "A comparative study on unsupervised feature selection methods for text clustering," in Proc. IEEE Int. Conf. Natural Language Processing and Knowledge Engineering, 2005, pp. 597-601.
17. V. Levenshtein, "Binary codes capable of correcting deletions, insertions, and reversals," Soviet Physics Doklady, vol. 10, pp. 707-710, 1966.
18. B. Mirkin, Clustering for Data Mining: A Data Recovery Approach. London, U.K.: Chapman & Hall, 2005.
19. A. L. N. Fred and A. K. Jain, "Combining multiple clusterings using evidence accumulation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 6, pp. 835-850, Jun. 2005. (Pubitemid 40889836)
20. L. Hubert and P. Arabie, "Comparing partitions," J. Classification, vol. 2, pp. 193-218, 1985.
21. C. M. Bishop, Pattern Recognition and Machine Learning. New York: Springer-Verlag, 2006.
22. S. Haykin, Neural Networks: A Comprehensive Foundation. Englewood Cliffs, NJ: Prentice-Hall, 1998.
23. L. F. Nassif and E. R. Hruschka, "Document clustering for forensic computing: An approach for improving computer inspection," in Proc. Tenth Int. Conf. Machine Learning and Applications (ICMLA), 2011, vol. 1, pp. 265-268, IEEE Press.
24. Aggarwal, C. C. Charu, and C. X. Zhai, Eds., "Chapter 4: A Survey of Text Clustering Algorithms," in Mining Text Data. NewYork: Springer, 2012.
25. Y. Zhao, G. Karypis, and U. M. Fayyad, "Hierarchical clustering algorithms for document datasets," Data Min. Knowl. Discov., vol. 10, no. 2, pp. 141-168, 2005. (Pubitemid 41217849)
26. Y. Zhao and G. Karypis, "Evaluation of hierarchical clustering algorithms for document datasets," in Proc. CIKM, 2002, pp. 515-524.
27. S. Nassar, J. Sander, and C. Cheng, "Incremental and effective data summarization for dynamic hierarchical clustering," in Proc. 2004 ACM SIGMOD Int. Conf. Management of Data (SIGMOD '04), 2004, pp. 467-478.
28. K. Kishida, "High-speed rough clustering for very large document collections," J. Amer. Soc. Inf. Sci., vol. 61, pp. 1092-1104, 2010, doi: 10.1002/asi.2131.
29. Y. Loewenstein, E. Portugaly, M. Fromer, and M. Linial, "Effcient algorithms for exact hierarchical clustering of huge datasets: Tackling the entire protein space," Bioinformatics, vol. 24, no. 13, pp. i41-i49, 2008. (Pubitemid 351911656)