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AI & SOCIETY

, Volume 33, Issue 2, pp 261–274 | Cite as

GDP growth vs. criminal phenomena: data mining of Japan 1926–2013

  • Xingan Li
  • Henry Joutsijoki
  • Jorma Laurikkala
  • Martti JuholaEmail author
Open Forum

Abstract

The aim of this article is to inquire about potential relationship between change of crime rates and change of gross domestic product (GDP) growth rate, based on historical statistics of Japan. This national-level study used a dataset covering 88 years (1926–2013) and 13 attributes. The data were processed with the self-organizing map (SOM), separation power checked by our ScatterCounter method, assisted by other clustering methods and statistical methods for obtaining comparable results. The article is an exploratory application of the SOM in research of criminal phenomena through processing of multivariate data. The research confirmed previous findings that SOM was able to cluster efficiently the present data and characterize these different clusters. Other machine learning methods were applied to ensure clusters computed with SOM. The correlations obtained between GDP and other attributes were mostly weak, with a few of them interesting.

Keywords

Data mining Self-organizing map Classification methods Japan GDP growth rate Crime rate Development of criminal phenomena 

Notes

Acknowledgements

The second author is thankful for the Finnish Cultural Foundation Pirkanmaa Regional Fund for the support.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Abe S (2010) Support vector machines for pattern classification, 2nd edn. Springer, LondonCrossRefzbMATHGoogle Scholar
  2. Adderley R (2004) The use of data mining techniques in operational crime fighting. In: Proceedings of symposium on intelligence and security informatics, Tucson A.Z., ETATS-UNIS (10/06/2004), vol 3073 (2), pp 418–425Google Scholar
  3. Adderley R, Musgrave P (2003) Modus operandi modelling of group offending: a data-mining case study. Int J Police Sci Manag 5(4):265–276CrossRefGoogle Scholar
  4. Axelsson S (2005) Understanding intrusion detection through visualization. PhD thesis, Chalmers University of Technology, Göteborg, SwedenGoogle Scholar
  5. Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefzbMATHGoogle Scholar
  6. Brockett PL, Xia X, Derrig RA (1998) Using Kohonen’s self-organizing feature map to uncover automobile bodily injury claims fraud. J Risk Insur 65(2):245–274CrossRefGoogle Scholar
  7. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297zbMATHGoogle Scholar
  8. Fei B, Eloff J, Venter H, Olivier (2005) Exploring data generated by computer forensic tools with self-organising maps. Proceedings of the IFIP working group 11.9 on digital forensics, pp 1–15Google Scholar
  9. Fei B, Eloff J, Olivier M, Venter H (2006) The use of self-organizing maps for anomalous behavior detection in a digital investigation. Forensic Sci Int 162(1–3):33–37CrossRefGoogle Scholar
  10. Grosser H, Britos P, García-Martínez R (2005) Detecting fraud in mobile telephony using neural networks. In: Ali M, Esposito F (eds) Lecture notes in artificial intelligence, vol 3533. Springer, Berlin, pp 613–615Google Scholar
  11. Hollmén J (2000) User profiling and classification for fraud detection in mobile communications networks. PhD thesis, Helsinki University of Technology, FinlandGoogle Scholar
  12. Hollmén J, Tresp V, Simula O (1999) A self-organizing map for clustering probabilistic models. Artif Neural Netw 470:946–951Google Scholar
  13. Hsu C-W, Lin C-J (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425CrossRefGoogle Scholar
  14. Juhola M, Siermala M (2012) A scatter method for data and variable importance evaluation. Integr Comp Aided Eng 19:137–149Google Scholar
  15. Kangas LJ (2001) Artificial neural network system for classification of offenders in murder and rape cases. The National Institute of Justice, FinlandGoogle Scholar
  16. Kangas LJ, Terrones KM, Keppel RD, La Moria RD (1999) Computer-aided tracking and characterization of homicides and sexual assaults (CATCH). Proc. SPIE 3722, applications and science of computational intelligence IIGoogle Scholar
  17. Kohonen T (1979) Self-organizing maps. Springer, New YorkzbMATHGoogle Scholar
  18. Lampinen T, Koivisto H, Honkanen T (2005) Profiling network applications with fuzzy C-means and self-organizing maps. Classif Clust Knowl Discov 4:15–27Google Scholar
  19. Lei H, Govindaraju V (2005) Half-Against-Half multi-class support vector machines. In: Proceedings of the 6th international workshop on multiple classifier systems, Lecture Notes Comp Sci, vol 3541, pp 156–164Google Scholar
  20. Leufven C (2006) Detecting SSH identity theft in HPC cluster environments using self-organizing maps. Master thesis, Linköping University, SwedenGoogle Scholar
  21. Li X (2014) Application of data mining methods in the study of crime based on international data sources. PhD thesis, University of Tampere, Tampere, FinlandGoogle Scholar
  22. Li X, Juhola M (2013) Crime and its social context: analysis using the self-organizing map. In: Intelligence and security informatics conference (EISIC), 2013 European, 12–14 Aug. 2013, Uppsala, Sweden, IEEE, pp 121–124Google Scholar
  23. Li X, Juhola M (2014a) Country crime analysis using the self-organizing map, with special regard to demographic factors. Artif Intell Soc 29(1):53–68Google Scholar
  24. Li X, Juhola M (2014b) Application of the self-organising map to visulisation of and exploration into historical development of criminal phenomena of the USA, 1960–2007. Int J Soc Syst Sci 6(2):120–142CrossRefGoogle Scholar
  25. Li X, Juhola M (2015) Country crime analysis using the self-organising map, with special regard to economic factors. Int J Data Mining Model Manag 7(2):130–153Google Scholar
  26. Li X, Joutsijoki H, Laurikkala J, Siermala M, Juhola M (2015a) Crime vs. demographic factors revisited: application of data mining methods. Webology 12(1), Article 132. Retrieved June 11, 2016, from http://www.webology.org/2015/v12n1/a132.pdf
  27. Li X, Joutsijoki H, Laurikkala J, Siermala M, Juhola M (2015b) Homicide and its social context: analysis using the self-organizing map. Appl Artif Intell 29(4):382–401CrossRefGoogle Scholar
  28. Memon QA, Mehboob S (2006) Crime investigation and analysis using neural nets. In: Proceedings of international joint conference on neural networks, Washington, D.C., pp 346–350Google Scholar
  29. Platt JC, Christiani N, Shawe-Taylor J (2000) Large margin DAGs for multiclass classification. Adv Neural Inform Process Syst 12:547–553Google Scholar
  30. Rifkin R, Klautau A (2004) In defense of one-vs-all classification. J Mach Learn Res 5:101–141MathSciNetzbMATHGoogle Scholar
  31. Siermala M, Juhola M (2006) Techniques for biased data distribution and variable classification with neural networks applied to otoneurological data. Comput Methods Progr Biomed 81(2):128–136CrossRefGoogle Scholar
  32. Siermala M, Juhola M, Laurikkala J, Iltanen K, Kentala E, Pyykkö I (2007) Evaluation and classification of otoneurological data with new data analysis methods based on machine learning. Inf Sci 177(9):1963–1976CrossRefGoogle Scholar
  33. Suykens JAK, Vandewalle J (1999a) Least squares support vector machines. Neural Process Lett 9(3):293–300CrossRefzbMATHGoogle Scholar
  34. Suykens JAK, Vandewalle J (1999b) Multiclass least squares support vector machines. Proc Int Joint Conf Neural Netw 2:900–903CrossRefzbMATHGoogle Scholar
  35. Suykens JAK, van Gestel T, De Brabanter J, De Moor B, Vandewalle J (2002) Least squares support vector machines. World Scientific, HackensackCrossRefzbMATHGoogle Scholar
  36. Takahashi F, Abe S (2002) Decision-tree-based multiclass support vector machines. Proceedings of the 9th international conference on neural information processing, vol 3, pp 1418–1422Google Scholar
  37. Vapnik VN (2000) The nature of statistical learning theory, 2nd edn. Springer, New YorkCrossRefzbMATHGoogle Scholar
  38. Viscovery Software GmbH (2015) Viscovery SOMine. http://www.viscovery.net/somine/
  39. Zaslavsky V, Strizhak A (2006) Credit card fraud detection using self-organizing maps. Inf Secur 18:48–63Google Scholar

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  • Xingan Li
    • 1
  • Henry Joutsijoki
    • 2
  • Jorma Laurikkala
    • 2
  • Martti Juhola
    • 2
    Email author
  1. 1.School of Governance, Law and SocietyTallinn UniversityTallinnEstonia
  2. 2.Computer Science, Faculty of Natural SciencesUniversity of TampereTampereFinland

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