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Semantic Document Clustering for Crime Investigation


Semantic Document Clustering for Crime Investigation

Daghir, Kabi (2011) Semantic Document Clustering for Crime Investigation. Masters thesis, Concordia University.

Text (application/pdf)
Daghir_MASc_F2011.pdf - Accepted Version


Computers are increasingly used as tools to commit crimes such as unauthorized access (hacking), drug trafficking, and child pornography. The proliferation of crimes involving
computers has created a demand for special forensic tools that allow investigators to look for evidence on a suspect’s computer by analyzing communications and data on the computer’s storage devices. Motivated by the forensic process at Sûreté du Québec (SQ), the Québec provincial police, we propose a new subject-based semantic document clustering model that allows an investigator to cluster documents stored on a suspect’s computer by grouping them into a set of overlapping clusters, each corresponding to a subject of interest initially defined by the investigator.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Daghir, Kabi
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Information Systems Security
Date:12 September 2011
Thesis Supervisor(s):Fung, Benjamin
Keywords:clustering, classification, data mining, information retrieval, forensic analysis, crime investigation
ID Code:35765
Deposited By: KABI DAGHIR
Deposited On:17 Nov 2011 19:06
Last Modified:18 Jan 2018 17:35
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