Skip to main content
SpringerLink
Log in

Crime linkage and psychological profiling of offenders under intuitionistic fuzzy environment using a novel resemblance measure

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Crime is a significant issue in society, with the causes of crime needing more attention and action from social, governmental, and judicial entities. Investigating crimes can be challenging due to uncertainties and unreliable evidence. Crime linkage helps investigators identify and solve crimes committed by the same individuals or groups. Fuzzy sets and intuitionistic fuzzy sets have been helpful in decision-making problems related to crime linkage due to the uncertainty involved. Similarity measures are essential in decision-making problems, but the existing measures must be revised when dealing with three or more intuitionistic fuzzy sets. The proposed resemblance measure based on intuitionistic fuzzy sets can find similarities between more than two sets and be used in the crime linkage. The proposed measure’s superiority is demonstrated through examples and applied to crime linkage through case studies. A methodology for the psychological profiling of offenders is also presented through case studies. These proposed methods can help law enforcement solve decision-making problems related to crime linkage and psychological profiling.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Adeyiga JA, Bello A (2016) A review of different clustering techniques in criminal profiling. Int J Adv Res Comput Sci Softw Eng 6(4):659–666

    Google Scholar 

  • Albertetti, F., Cotofrei, P., Grossrieder, L., Ribaux, O., & Stoffel, K. (2013, August). The CriLiM methodology: crime linkage with a fuzzy MCDM approach. In 2013 European Intelligence and Security Informatics Conference (pp. 67-74). IEEE

  • Alikhademi K, Drobina E, Prioleau D, Richardson B, Purves D, Gilbert JE (2021) A review of predictive policing from the perspective of fairness. Artif Intel Law. https://doi.org/10.1007/s10506-021-09286-4

    Article  Google Scholar 

  • Alison L, Bennell C, Mokros A, Ormerod D (2002) The personality paradox in offender profiling: a theoretical review of the processes involved in deriving background characteristics from crime scene actions. Psychol Public Policy Law 8(1):115

    Google Scholar 

  • Altameem T, Amoon M (2019) Crime activities prediction using hybridization of firefly optimization technique and fuzzy cognitive map neural networks. Neural Comput Appl 31(5):1263–1273

    Google Scholar 

  • Ashraf Z, Khan MS, Tiwari A, Danish Lohani QM (2021) Difference sequence-based distance measure for intuitionistic fuzzy sets and its application in decision making process. Soft Comput 25(14):9139–9161

    MATH  Google Scholar 

  • Atanassov, K. T. (1999). Intuitionistic fuzzy sets. In Intuitionistic fuzzy sets (pp. 1-137). Physica, Heidelberg

  • Atanassov K (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96

    MathSciNet  MATH  Google Scholar 

  • Atanassov KT (2012) On intuitionistic fuzzy sets theory, vol 283. Springer, NY

    MATH  Google Scholar 

  • Augustine EP (2021) Novel correlation coefficient for intuitionistic fuzzy sets and its application to multi-criteria decision-making problems. Int J Fuzzy Syst Appl (IJFSA) 10(2):39–58

    Google Scholar 

  • Aziz RM, Baluch MF, Patel S, Kumar P (2022) A machine learning based approach to detect the ethereum fraud transactions with limited attributes. Karbala Int J Modern Sci 8(2):139–151

    Google Scholar 

  • Aziz RM, Baluch MF, Patel S, Ganie AH (2022) LGBM: a machine learning approach for Ethereum fraud detection. Int J Inf Technol. https://doi.org/10.1007/s41870-022-00864-6

    Article  Google Scholar 

  • Aziz RM, Hussain A, Sharma P, Kumar P (2022) Machine learning-based soft computing regression analysis approach for crime data prediction. Karb Int J Mod Sci 8(1):1–19

    Google Scholar 

  • Bartoletti M, Carta S, Cimoli T, Saia R (2020) Dissecting Ponzi schemes on ethereum: identification, analysis, and impact. Future Gene Comput Syst 102:259–277

    Google Scholar 

  • Beasley JO (2004) Serial murder in America: case studies of seven offenders. Behav Sci Law 22(3):395–414

    Google Scholar 

  • Beauregard E, Lussier P, Proulx J (2005) The role of sexual interests and situational factors on rapists’ modus operandi: implications for offender profiling. Leg Criminol Psychol 10(2):265–278

    Google Scholar 

  • Bhandari D, Pal NR (1993) Some new information measures for fuzzy sets. Inf Sci 67(3):209–228

    MathSciNet  MATH  Google Scholar 

  • Bin Sulaiman R, Schetinin V, Sant P (2022) Review of machine learning approach on credit card fraud detection. Human-Centric Intel Syst 2:55–68

    Google Scholar 

  • Boran FE, Akay D (2014) A biparametric similarity measure on intuitionistic fuzzy sets with applications to pattern recognition. Inf Sci 255:45–57

    MathSciNet  MATH  Google Scholar 

  • Bortolan G, Degani R (1985) A review of some methods for ranking fuzzy subsets. Fuzzy Sets Syst 15(1):1–19

    MathSciNet  MATH  Google Scholar 

  • Brayne S (2020) Predict and surveil: Data, discretion, and the future of policing. Oxford University Press, USA

    Google Scholar 

  • Bustince H, Burillo P (1996) Vague sets are intuitionistic fuzzy sets. Fuzzy Sets Syst 79(3):403–405

    MathSciNet  MATH  Google Scholar 

  • Canter, D. (1995). Psychology of offender profiling. W: Bull R, Carson D, red. Handbook of psychology in legal contexts

  • Chen SM (1997) Similarity measures between vague sets and between elements. IEEE Trans Syst , Man, Cybern B 27(1):153–158

    Google Scholar 

  • Chen C, Deng X (2020) Several new results based on the study of distance measures of intuitionistic fuzzy sets. Iranian J Fuzzy Syst 17(2):147–163

    MATH  Google Scholar 

  • Chen W, Zheng Z, Ngai ECH, Zheng P, Zhou Y (2019) Exploiting blockchain data to detect smart ponzi schemes on ethereum. IEEE Access 7:37575–37586

    Google Scholar 

  • Chu CH, Yen CP, Lin YF (2020) The fourth axiom of similarity measures. Symmetry 12(10):1735

    Google Scholar 

  • Copson G (1995) Coals to Newcastle? Part 1: A study of offender profiling. Home Office, UK

    Google Scholar 

  • Daves A (1991) The use of DNA profiling and behavioural science in the investigation of sexual offences. Med Sci Law 31(2):95–101

    Google Scholar 

  • De Luca, A., & Termini, S. (1993). A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory. In Readings in Fuzzy Sets for Intelligent Systems (pp. 197-202). Morgan Kaufmann

  • De SK, Biswas R, Roy AR (2001) An application of intuitionistic fuzzy sets in medical diagnosis. Fuzzy Sets Syst 117(2):209–213

    MATH  Google Scholar 

  • Dengfeng L, Chuntian C (2002) New similarity measures of intuitionistic fuzzy sets and application to pattern recognitions. Pattern Recognit Lett 23(1–3):221–225

    MATH  Google Scholar 

  • Dhivya J, Sridevi B (2019) A novel similarity measure between intuitionistic fuzzy sets based on the mid points of transformed triangular fuzzy numbers with applications to pattern recognition and medical diagnosis. Appl Math-A 34(2):229–252

    MathSciNet  MATH  Google Scholar 

  • Diamond P, Kloeden P (1990) Metric spaces of fuzzy sets. Fuzzy Sets Syst 35(2):241–249

    MathSciNet  MATH  Google Scholar 

  • Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302

    Google Scholar 

  • Douglas JE, Burgess AE (1986) Criminal profiling: a viable investigative tool against violent crime. FBI L Enforcement Bull 55:9

    Google Scholar 

  • Ejegwa PA (2015) Intuitionistic fuzzy sets approach in appointment of positions in an organization via max-min-max rule. Global J Sci Front Res: F Math Decision Sci 15(6):1–6

    Google Scholar 

  • Ejegwa PA, Modom ES (2015) Diagnosis of viral hepatitis using new distance measure of intuitionistic fuzzy sets. Int J Fuzzy Math Arch 8(1):1–7

    Google Scholar 

  • Ejegwa PA, Onyeke IC (2018) An object oriented approach to the application of intuitionistic fuzzy sets in competency based test evaluation. Ann Commun Math 1(1):38–47

    Google Scholar 

  • Ejegwa PA, Onyeke IC (2021) Intuitionistic fuzzy statistical correlation algorithm with applications to multicriteria-based decision-making processes. Int J Intell Syst 36(3):1386–1407

    Google Scholar 

  • Ejegwa PA, Akubo AJ, Joshua OM (2014) Intuitionistic fuzzzy sets in career determination. J Inf Comput Sci 9(4):285–288

    Google Scholar 

  • Farjami Y, Abdi K (2021) A genetic-fuzzy algorithm for spatio-temporal crime prediction. J Ambient Intel Human Comput. https://doi.org/10.1007/s12652-020-02858-3

    Article  Google Scholar 

  • Fu R, Wang Y, Jia C (2022) Data augmentation for cross-subject EEG features using Siamese neural network. Biomed Signal Process Control 75:103614

    Google Scholar 

  • Garg H (2018) An improved cosine similarity measure for intuitionistic fuzzy sets and their applications to decision-making process. Hacettepe J Math Stat 47(6):1578–1594

    MathSciNet  MATH  Google Scholar 

  • Garg H, Kumar K (2018) A novel correlation coefficient of intuitionistic fuzzysets based on the connection number of set pair analysis and its application. Scientia Iranica 25(4):2373–2388

    Google Scholar 

  • Garg H, Kumar K (2018) An advanced study on the similarity measures of intuitionistic fuzzy sets based on the set pair analysis theory and their application in decision making. Soft Comput 22(15):4959–4970

    MATH  Google Scholar 

  • Garg H, Rani D (2021) Novel similarity measure based on the transformed right-angled triangles between intuitionistic fuzzy sets and its applications. Cognit Comput 13(2):447–465

    Google Scholar 

  • Garg H, Rani D (2022) Novel distance measures for intuitionistic fuzzy sets based on various triangle centers of isosceles triangular fuzzy numbers and their applications. Expert Syst Appl 191:116228

    Google Scholar 

  • Garg H, Arora R, Arora R (2020) TOPSIS method based on correlation coefficient for solving decision-making problems with intuitionistic fuzzy soft set information. AIMS Math 5(4):2944–2966

    MathSciNet  MATH  Google Scholar 

  • Gau WL, Buehrer DJ (1993) Vague sets. IEEE Trans Syst Man Cybern 23(2):610–614

    MATH  Google Scholar 

  • Goala S, Dutta P (2018) A fuzzy multicriteria decision-making approach to crime linkage. Int J Inf Technol Syst Approach (IJITSA) 11(2):31–50

    Google Scholar 

  • Goala S, Dutta P (2018) Detection of area under potential threat via an advanced aggregation operator on generalized triangular fuzzy number. J Taibah Univ Sci 12(5):536–544

    Google Scholar 

  • Goala S, Dutta P, Talukdar P (2019) Intuitionistic fuzzy multi criteria decision making approach to crime linkage using resemblance function. Int J Appl Comput Math 5(4):1–17

    MATH  Google Scholar 

  • Gogoi, S., Gohain, B., & Chutia, R. (2022). Distance measures on intuitionistic fuzzy sets based oncross-information dissimilarity and their diverse applications

  • Gohain B, Dutta P, Gogoi S, Chutia R (2021) Construction and generation of distance and similarity measures for intuitionistic fuzzy sets and various applications. Int J Intel Syst 12:7805–7838

    Google Scholar 

  • Gohain B, Chutia R, Dutta P (2022) Distance measure on intuitionistic fuzzy sets and its application in decision-making, pattern recognition, and clustering problems. Int J Int Syst 37(3):2458–2501

    Google Scholar 

  • Gohain B, Chutia R, Dutta P, Gogoi S (2022) Two new similarity measures for intuitionistic fuzzy sets and its various applications. Int J Intel Syst 37(9):5557–5596

    Google Scholar 

  • Grubesic TH (2006) On the application of fuzzy clustering for crime hot spot detection. J Quant Criminol 22(1):77–105

    Google Scholar 

  • Gupta, S., & Kumar, S. (2015). Crime detection and prevention using social network analysis. Int J Comput Appl, 126(6)

  • Gupta VK, Shukla SK, Rawat RS (2022) Crime tracking system and people’s safety in India using machine learning approaches. Int J Modern Res 2(1):1–7

    Google Scholar 

  • Halford E (2022) Linking foraging domestic burglary: an analysis of crimes committed within police-identified optimal forager patches. J Police Criminal Psychol 38:127–140

    Google Scholar 

  • Hazelwood RR, Warren JI (2003) Linkage analysis: Modus operandi, ritual, and signature in serial sexual crime. Aggress Violent Beh 8(6):587–598

    Google Scholar 

  • Holmes RM, Holmes ST (2008) Profiling violent crimes: an investigative tool. Sage, UK

    Google Scholar 

  • Hong DH, Kim C (1999) A note on similarity measures between vague sets and between elements. Inf Sci 115(1–4):83–96

    MathSciNet  MATH  Google Scholar 

  • Hung WL, Yang MS (2004) Similarity measures of intuitionistic fuzzy sets based on Hausdorff distance. Pattern Recognit Lett 25(14):1603–1611

    Google Scholar 

  • Hwang CM, Yang MS, Hung WL (2018) New similarity measures of intuitionistic fuzzy sets based on the Jaccard index with its application to clustering. Int J Intel Syst 33(8):1672–1688

    Google Scholar 

  • Jaccard P (1901) Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines. Bull Soc Vaudoise Sci Nat 37:241–272

    Google Scholar 

  • Jiang Q, Jin X, Lee SJ, Yao S (2019) A new similarity/distance measure between intuitionistic fuzzy sets based on the transformed isosceles triangles and its applications to pattern recognition. Expert Syst Appl 116:439–453

    Google Scholar 

  • Keppel R (2010) The riverman: Ted Bundy and I hunt for the green river killer. Simon and Schuster, NY

    Google Scholar 

  • Keshavarz Ghorabaee M, Zavadskas EK, Olfat L, Turskis Z (2015) Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica 26(3):435–451

    Google Scholar 

  • Knight RA, Warren JI, Reboussin R, Soley BJ (1998) Predicting rapist type from crime-scene variables. Crim Justice Behav 25(1):46–80

    Google Scholar 

  • Kotsoglou KN, Oswald M (2020) The long arm of the algorithm? Automated facial recognition as evidence and trigger for police intervention. Forensic Sci Int: Synerg 2:86–89

    Google Scholar 

  • Li, R. J., & Lee, E. S. (1987). Ranking fuzzy numbers-A comparison. Proceedings of NAFIPS, May, 5(7)

  • Li F, Xu Z (2001) Measures of similarity between vague sets. J Softw 12(6):922–927

    Google Scholar 

  • Li ST, Kuo SC, Tsai FC (2010) An intelligent decision-support model using FSOM and rule extraction for crime prevention. Expert Syst Appl 37(10):7108–7119

    Google Scholar 

  • Liang Z, Shi P (2003) Similarity measures on intuitionistic fuzzy sets. Pattern Recognit Lett 24(15):2687–2693

    MATH  Google Scholar 

  • Luo X, Li W, Zhao W (2018) Intuitive distance for intuitionistic fuzzy sets with applications in pattern recognition. Appl Intel 48(9):2792–2808

    Google Scholar 

  • Mahanta J, Panda S (2021) A novel distance measure for intuitionistic fuzzy sets with diverse applications. Int J Intel Syst 36(2):615–627

    Google Scholar 

  • Mitchell HB (2003) On the Dengfeng-Chuntian similarity measure and its application to pattern recognition. Pattern Recognit Lett 24(16):3101–3104

    Google Scholar 

  • Mokros A, Alison LJ (2002) Is offender profiling possible? Testing the predicted homology of crime scene actions and background characteristics in a sample of rapists. Leg Criminol Psychol 7(1):25–43

    Google Scholar 

  • Muller DA (2000) Criminal profiling: real science or just wishful thinking? Homicide Stud 4(3):234–264

    MathSciNet  Google Scholar 

  • Nagamani, M. S., Bhavishya, M. I., Kumar, M. B. V., & Sree, M.T.G. (2020). An experimental study of Crime Prediction using Machine Learning Algorithms

  • Nath SV (2007) Crime data mining. Advances and Innovations in Systems, Computing Sciences and Software Engineering. Springer, Dordrecht, pp 405–409

    Google Scholar 

  • Ngan RT, Cuong BC, Ali M (2018) H-max distance measure of intuitionistic fuzzy sets in decision making. Appl Soft Comput 69:393–425

    Google Scholar 

  • Nguyen, X. T., & Chou, S. Y. (2021). Novel similarity measures, entropy of intuitionistic fuzzy sets and their application in software quality evaluation

  • O’Brien D (2014) The hillside stranglers: The inside story of the killing spree that terrorized Los Angeles. Open Road Media, NY

    Google Scholar 

  • Ohlan A (2016) Similarity measures on intuitionistic fuzzy sets. Int J Sci Technol Manag 5(12):463–468

    MATH  Google Scholar 

  • Ormerod, D. (1999). Criminal profiling: Trial by judge and jury, not criminal psychologist. Profiling in policy and practice, 207-261

  • Quek C, Tan KB, Sagar VK (2001) Pseudo-outer product based fuzzy neural network fingerprint verification system. Neural Netw 14(3):305–323

    Google Scholar 

  • Salton G, McGill MJ (1983) Introduction to modern information retrieval. Mcgraw-hill, NY

    MATH  Google Scholar 

  • Simmler M, Canova G, Schedler K (2021) Smart criminal justice: phenomena and normative requirements. Int Rev Admin Sci 89(2):415–432

    Google Scholar 

  • Sreedevi M, Reddy AHV, Reddy C (2018) Review on crime analysis and prediction using data mining techniques. Int J Innov Res Sci , Eng Technol 7(4):3360–3369

    Google Scholar 

  • Stoffel K, Cotofrei P, Han D (2012) Fuzzy clustering based methodology for multidimensional data analysis in computational forensic domain. Int J Comput Inf Syst Ind Manag Appl 4:400–410

    Google Scholar 

  • Szmidt E, Kacprzyk J (2004) Medical diagnostic reasoning using a similarity measure for intuitionistic fuzzy sets. Note IFS 10(4):61–69

    MATH  Google Scholar 

  • Thao NX, Ali M, Smarandache F (2019) An intuitionistic fuzzy clustering algorithm based on a new correlation coefficient with application in medical diagnosis. J Intel Fuzzy Syst 36(1):189–198

    Google Scholar 

  • Vlachos IK, Sergiadis GD (2007) Intuitionistic fuzzy information-applications to pattern recognition. Pattern Recognit Lett 28(2):197–206

    Google Scholar 

  • Wang W, Xin X (2005) Distance measure between intuitionistic fuzzy sets. Pattern Recognit Lett 26(13):2063–2069

    Google Scholar 

  • Wheeler AP, Steenbeek W (2021) Mapping the risk terrain for crime using machine learning. J Quant Criminol 37(2):445–480

    Google Scholar 

  • Xiao F (2019) A distance measure for intuitionistic fuzzy sets and its application to pattern classification problems. IEEE Trans Syst, Man, and Cybern: Syst 51(6):3980–3992

    Google Scholar 

  • Xuan Thao N (2018) A new correlation coefficient of the intuitionistic fuzzy sets and its application. J Intel Fuzzy Syst 35(2):1959–1968

    Google Scholar 

  • Yang J, Wang G, Zhang Q (2018) Knowledge distance measure in multigranulation spaces of fuzzy equivalence relations. Inf Sci 448:18–35

    MathSciNet  MATH  Google Scholar 

  • Yang J, Wang G, Zhang Q, Wang H (2019) Knowledge distance measure for the multigranularity rough approximations of a fuzzy concept. IEEE Trans Fuzzy Syst 28(4):706–717

    Google Scholar 

  • Ye J (2011) Cosine similarity measures for intuitionistic fuzzy sets and their applications. Math Comput Model 53(1–2):91–97

    MathSciNet  MATH  Google Scholar 

  • Ye J (2012) Multicriteria group decision-making method using vector similarity measures for trapezoidal intuitionistic fuzzy numbers. Group Decis Negot 21(4):519–530

    Google Scholar 

  • Zadeh LA (1965) Inf Control 8(3):338–353

    Google Scholar 

  • Zhang H (2014) Linguistic intuitionistic fuzzy sets and application in MAGDM. J Appl Math. https://doi.org/10.1155/2014/432092

    Article  MATH  Google Scholar 

  • Zhang Z, Huang J, Hao J, Gong J, Chen H (2020) Extracting relations of crime rates through fuzzy association rules mining. Appl Intel 50(2):448–467

    Google Scholar 

Download references

Acknowledgements

The authors greatly acknowledge the support of the DST-PURSE Programme SR/PURSE/2022/143(1)

Author information

Authors and Affiliations

  1. Department of Mathematics, Dibrugarh University, Dibrugarh, Assam, 786004, India

    Palash Dutta & Abhilash Kangsha Banik

Authors
  1. Palash Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abhilash Kangsha Banik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Palash Dutta.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dutta, P., Banik, A.K. Crime linkage and psychological profiling of offenders under intuitionistic fuzzy environment using a novel resemblance measure. Artif Intell Rev 56 (Suppl 1), 893–936 (2023). https://doi.org/10.1007/s10462-023-10538-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-023-10538-9

Keywords

Search

Navigation