Fuzzy Clustering based Methodology for Multidimensional Data Analysis in Computational Forensic Domain

As interdisciplinary domain requiring advanced and innovative methodologies, the computational forensics domain is characterized by data being, simultaneously, large scaled and uncertain, multidimensional and approximate. Forensic domain experts, trained to discover hidden pattern from crime data, are limited in their analysis without the assistance of a computational intelligence approach. In this paper, a methodology and an automatic procedure, based on fuzzy set theory and designed to infer precise and intuitive expert-system-like rules from original forensic data, is described. The main steps of the methodology are detailed, as well as the experiments conducted on forensic data sets both simulated data and real data, representing robberies and residential burglaries.

[1]  Ryan Kling,et al.  Investigative Profile Analysis With Computer Forensic Log Data Using Attribute Generalisation , 2002, Australasian Data Mining Conference.

[2]  J. Wu,et al.  A genetic fuzzy k-Modes algorithm for clustering categorical data , 2009, Expert Syst. Appl..

[3]  Hany H. Ammar,et al.  A neural network system for matching dental radiographs , 2007, Pattern Recognit..

[4]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[5]  Satoru Fukami,et al.  Some considerations on fuzzy conditional inference , 1980 .

[6]  Andrew H. Sung,et al.  Identifying Significant Features for Network Forensic Analysis Using Artificial Intelligence Techniques , 2003, Int. J. Digit. EVid..

[7]  Ricci S. C. Ieong,et al.  FORZA - Digital forensics investigation framework that incorporate legal issues , 2006, Digit. Investig..

[8]  Sheng-Tun Li,et al.  An intelligent decision-support model using FSOM and rule extraction for crime prevention , 2010, Expert Syst. Appl..

[9]  J A Lambert,et al.  Using a tree diagram to interpret a mixed DNA profile. , 1998, Journal of forensic sciences.

[10]  Gang Wang,et al.  Crime data mining: a general framework and some examples , 2004, Computer.

[11]  Tinghua Wang,et al.  Network forensics based on fuzzy logic and expert system , 2009, Comput. Commun..

[12]  M. Sugeno.  FUZZY MEASURES AND FUZZY INTEGRALS—A SURVEY , 1993 .

[13]  Sargur N. Srihari,et al.  Computational Forensics: Towards Hybrid-Intelligent Crime Investigation , 2007, Third International Symposium on Information Assurance and Security.

[14]  R. Yager.  On a general class of fuzzy connectives , 1980 .

[15]  Haoyuan Li,et al.  Discovery of Unexpected Fuzzy Recurrence Behaviors in Sequence Databases , 2010 .

[16]  Salvatore J. Stolfo,et al.  Data mining-based intrusion detectors: an overview of the columbia IDS project , 2001, SGMD.

[17]  Didier Dubois,et al.  Fuzzy sets and systems ' . Theory and applications , 2007 .

[18]  Gang Wang,et al.  Automatically detecting deceptive criminal identities , 2004, CACM.

[19]  P. Sinha.  A symmetry perceiving adaptive neural network and facial image recognition. , 1998, Forensic science international.

[20]  Megan Carney,et al.  The Trojan Made Me Do It: A First Step in Statistical Based Computer Forensics Event Reconstruction , 2004, Int. J. Digit. EVid..

[21]  Sridha Sridharan,et al.  A two stage fuzzy decision classifier for speaker identification , 1996, Speech Commun..

[22]  Shaio Yan Huang,et al.  Using the artificial neural network to predict fraud litigation: Some empirical evidence from emerging markets , 2009, Expert Syst. Appl..

[23]  Matthew M. Shannon.  Forensic Relative Strength Scoring: ASCII and Entropy Scoring , 2004, Int. J. Digit. EVid..

[24]  Khosrow Kaikhah,et al.  Discovering Trends in Large Datasets Using Neural Networks , 2006, Applied Intelligence.

[25]  Hiok Chai Quek,et al.  Pseudo-outer product based fuzzy neural network fingerprint verification system , 2001, Neural Networks.

[26]  Hsinchun Chen,et al.  Using Coplink to Analyze Criminal-Justice Data , 2002, Computer.

[27]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[28]  Xiyu Liu,et al.  A Novel Fuzzy Clustering Based on Particle Swarm Optimization , 2007, 2007 First IEEE International Symposium on Information Technologies and Applications in Education.

[29]  Anil K. Jain,et al.  On-line fingerprint verification , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[30]  Pierre Margot,et al.  The contribution of forensic science to crime analysis and investigation: forensic intelligence. , 2006, Forensic science international.

[31]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[32]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[33]  Tony H. Grubesic,et al.  On The Application of Fuzzy Clustering for Crime Hot Spot Detection , 2006 .

[34]  Rong Zheng,et al.  Crime Data Mining: An Overview and Case Studies , 2003, DG.O.

[35]  Sankar K. Pal,et al.  Rough Set Based Generalized Fuzzy $C$ -Means Algorithm and Quantitative Indices , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[36]  Sushil Jajodia,et al.  ADAM: a testbed for exploring the use of data mining in intrusion detection , 2001, SGMD.

[37]  George M. Mohay,et al.  Mining e-mail content for author identification forensics , 2001, SGMD.

[38]  Tamas Abraham,et al.  Investigative profiling with computer forensic log data and association rules , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[39]  菅野 道夫,et al.  Industrial applications of fuzzy control , 1985 .

[40]  W. B. Cavnar,et al.  N-gram-based text categorization , 1994 .

[41]  L. A. ZADEH,et al.  The concept of a linguistic variable and its application to approximate reasoning - I , 1975, Inf. Sci..

[42]  Hsinchun Chen,et al.  Extracting Meaningful Entities from Police Narrative Reports , 2002, DG.O.

[43]  Hsinchun Chen,et al.  Fighting organized crimes: using shortest-path algorithms to identify associations in criminal networks , 2004, Decis. Support Syst..