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Information Technology Convergence pp 531–538Cite as

A Decision Tree-Based Classification Model for Crime Prediction

Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 253)

Abstract

The growing availability of information technologies has enabled law enforcement agencies to collect detailed data about various crimes. Classification techniques can be applied to these data to build decision-aid tools and facilitate investigations of law enforcement agencies. In this paper, we propose an approach for constructing a decision tree based classification model for a crime prediction. Proposed model assists law enforcement agencies in discovering crime patterns and predicting future trends. We provide an implementation and analysis of our proposed method.

Keywords

  • Crime prediction
  • Classification
  • Decision tree user experience

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  • DOI: 10.1007/978-94-007-6996-0_56
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References

  1. Baumgartner K, Ferrari S, Palermo G (2008) Constructing Bayesian networks for criminal profiling from limited data. Knowl Based Syst 21(7):563–572

    CrossRef  Google Scholar 

  2. Fayyad UM, Piatetsky-Shapiro G, Smyth P (1996) The KDD process for extracting useful knowledge from volumes of data. Commun ACM (Special Issue on Data mining) 39(11):27–34

    Google Scholar 

  3. Lim N (2007) Classification by ensembles from random partitions using logistic regression models. Dissertation

    Google Scholar 

  4. Chen H, Chung W, Xu JJ, Wang G, Qin Y, Chau M (2004) Crime data mining: a general framework and some examples. The Comp 37(4):50–56

    Google Scholar 

  5. Liu X, Jian C, Lu CT (2010) A spatio-temporal-texture crime search engine. In: Proceeding of the 18th SIGSPATIAL international conference on advances in geographical information systems, pp 528–529

    Google Scholar 

  6. Shah S, Boa F, Lu CT, Chen IR. (2011) CROWDSAFE: Crowd sourcing of crime incidents and safe routing on mobile devices. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographical information systems, pp 521–524

    Google Scholar 

  7. Wang B, Dong H, Boedihardjo AP, Lu CT, Yu H, Chen IR, Dai J (2012) An integrated framework for spatio-temporal-textual search and mining. In: Proceeding of the 20th SIGSPATIAL international conference on advances in geographical information systems, pp 570–573

    Google Scholar 

  8. Yousef M, Najami N, Khalifa WA (2010) A comparison study between one-class and two-class machine learning for MicroRNA target detection. J. Biomed. Sci. Eng. 247–252

    Google Scholar 

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Acknowledgments

This work was supported by the IT R&D program of MKE/KEIT. [10041854, Development of a smart home service platform with real-time danger prediction and prevention for safety residential environments].

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Authors and Affiliations

  1. Department of Multimedia Science, Sookmyung Women’s University, Cheongpa-ro 47-gil 100, Yongsan-Ku, Seoul, 140-742, Korea

    Aziz Nasridinov, Sun-Young Ihm & Young-Ho Park

Authors
  1. Aziz Nasridinov
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  2. Sun-Young Ihm
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  3. Young-Ho Park
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Corresponding author

Correspondence to Aziz Nasridinov .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Seoul University of Science & and Technology (SeoulTech), Seoul, Korea, Republic of (South Korea)

    James J. (Jong Hyuk) Park

  2. Dept. Information & Communication Engineering, Fukuoka Institute of Technology Fac. Information Engineering, Fukuoka, Japan

    Leonard Barolli

  3. Departament De Llenguatges I Sistemes Informàtics, Universitat Politècnica De Catalunya, Barcelona, Spain

    Fatos Xhafa

  4. Humanitas College, Kyung Hee University, Seoul, Korea, Republic of (South Korea)

    Hwa-Young Jeong

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© 2013 Springer Science+Business Media Dordrecht

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Nasridinov, A., Ihm, SY., Park, YH. (2013). A Decision Tree-Based Classification Model for Crime Prediction. In: Park, J., Barolli, L., Xhafa, F., Jeong, HY. (eds) Information Technology Convergence. Lecture Notes in Electrical Engineering, vol 253. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6996-0_56

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  • DOI: https://doi.org/10.1007/978-94-007-6996-0_56

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-6995-3

  • Online ISBN: 978-94-007-6996-0

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