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BİRLİKTELİK KURAL ÇIKARIMI İLE SUÇ VERİ ANALİZİ

Year 2020, Volume: 2 Issue: 2, 42 - 50, 25.06.2021

Abstract

Küreselleşen Dünya’nın hayatımıza kattığı olumlu gelişmeler ile birlikte, toplum refahını ve düzenini bozan sosyal medya dolandırıcılığı, uyuşturucu ticareti, araç hırsızlığı vb. gibi yeni suç türleri de ortaya çıkmıştır. Bilişim teknolojisindeki gelişmeler sayesinde bu suçların konusu, konum ve zaman bilgileri, suç türü gibi olaya ilişkin çeşitli verilerin gerçek zamanlı kayıt altına alınabilmesi mümkün olmaktadır. Kayıt altına alınan bu ham verilerin çeşitli veri madenciliği yöntemleri kullanılarak analiz edilmesi ile veriyi tanımlayan veya öngörü amaçlı kullanılabilecek bilgilerin ortaya çıkartılması mümkündür. Bu çalışmada, veri madenciliği uygulamalarından R programı ile Apriori algoritması ve Rapidminer programı ile FP-Growth algoritması kullanılarak, ABD’nin Maryland eyaletinde 2016 yılının Temmuz ayından 2018 Nisan ayına kadar meydana gelen suç verilerinden oluşan NIBRS Crime veriseti üzerinde birliktelik kuralları analizi uygulaması gerçekleştirilmiştir. Oluşturulan bu birliktelik kuralları ile hangi saat aralıklarında, hangi semtte, ne tür suçların, ne sıklıkla gerçekleştirildiği analiz edilmiş ve algoritmaların sonuçları sunulmuştur. Bu analiz sonucunda çıkan sonuçlar ile güvenlik güçleri ve kolluk kuvvetleri gibi toplumun huzurunu ve düzenini korumakla görevli olan kuruluşların; hangi semtte, hangi suçların daha sık işlendiği veya suçluların hangi saat aralığında daha aktif olduğu gibi faydalı bilgileri takip etmesi mümkün olmaktadır.

References

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  • Yu, C. H., Ward, M. W., Morabito, M., & Ding, W. (2011, December). Crime forecasting using data mining techniques. In 2011 IEEE 11th international conference on data mining workshops (pp. 779-786). IEEE.
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  • Turvey, B. (2002). Inductive criminal profiling. In Criminal Profiling: An Introduction to Behavioral Evidence Analysis (pp. 21-33). Academic Press London.
  • Data.Gov: Montgomery County-America ,Crime, data.montgomerycountymd.gov,(March 2018).
  • Ünsal, Ö. (2011). Mesleki Alan Seçimlerinin Makine Öğrenmesi Algoritması Kullanılara Belirlenmesi (Doctoral dissertation, Yüksek Lisans Tezi, Gazi Üniversitesi, Bilişim Enstitüsü, Ankara).
  • Özkan, Y. (2008). Veri madenciliği yöntemleri. Papatya Yayıncılık Eğitim. Ergün, K. (n.d.). Veri Madenciliğine Giriş. Balıkesir Üniversitesi Mühendislik Fakültesi Endüstri Mühendisliği Bölümü., from http://kergun.baun.edu.tr/veri_madenciligi_ceng_hafta1.pdf,(June 2019)
  • Chen, H., Chung, W., Qin, Y., Chau, M., Xu, J. J., Wang, G., ... & Atabakhsh, H. (2003, May). Crime data mining: an overview and case studies. In Proceedings of the 2003 annual national conference on Digital government research (pp. 1-5).
  • Ozgul, F., Atzenbeck, C., Celik, A., & Erdem, Z. (2011, July). Incorporating data sources and methodologies for crime data mining. In Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics (pp. 176-180). IEEE.
  • Mason, S. J. (2019). Analysis of Virginia Crime Data of the Year 2016 Using Data Mining Techniques (Doctoral dissertation, North Carolina Agricultural and Technical State University).
  • Mittal, M., Goyal, L. M., Sethi, J. K., & Hemanth, D. J. (2019). Monitoring the impact of economic crisis on crime in India using machine learning. Computational Economics, 53(4), 1467-1485.
  • Thangamuthu, M. A., Vadivel, M. G., & Priyadharshini, M. A. (2019). Detecting Criminal Method using Data Mining.
  • Ma, L., Chen, Y., & Huang, H. (2010, November). AK-Modes: A weighted clustering algorithm for finding similar case subsets. In 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering (pp. 218-223). IEEE.
  • Agrawal, R., & Shafer, J. C. (1996). Parallel mining of association rules. IEEE Transactions on knowledge and Data Engineering, 8(6), 962-969.
  • Agrawal, R., Imieliński, T., & Swami, A. (1993, June). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD international conference on Management of data (pp. 207-216).
  • Agarwal, R., & Srikant, R. (1994, September). Fast algorithms for mining association rules. In Proc. of the 20th VLDB Conference (pp. 487-499).
  • Şeker, S. E. (n.d.). Apriori Algoritması. Bilgisayar Kavramlari, http://bilgisayarkavramlari.sadievrenseker.com/2011/09/07/apriori-algoritmasi/,(May 2019)
  • Zhao, Q., & Bhowmick, S. S. (2003). Association rule mining: A survey. Nanyang Technological University, Singapore, 135.
  • Alataş, B.(2003). Nicel Birliktelik Kurallarının Keşfinde Bulanık Mantık ve Genetik Algoritma Yaklaşımı.Yüksek Lisans Tezi, Fırat Üniversitesi, Elazığ, Türkiye.
  • Hasan, M.,Apriori Algorithm , https://www.slideshare.net/mainul_hs/apriori-algorithm- 44679525,(Nowember 2019)
  • Chanda, AK., Apriori Algorith https://www.slideshare.net/ashisface/apriori-algorithm- 34707400,(Nowember 2019)
  • Alataş, B., and Arslan, A., Mining of Fuzzy Association Rules with Genetic Algorithms, Journal of Polytechnic, Vol: 7, No: 4 pp. 269-276, 2004.
  • Birant, D., Kut, A., Ventura, M., Altınok, H., Altınok, B., Altınok, E., & Ihlamur, M. (2010). İş Zekası Çözümleri için Çok Boyutlu Birliktelik Kuralları Analizi. Akademik Bilişim, 10, 256.
  • Öğütücü, ŞG.,Veri Madenciliği İlişkilendirme Kuralları,https://web.itu.edu.tr/~sgunduz/courses/verimaden/slides/d7.pdf,(Nowember 2019)
  • Ödoğan, G., Abul, O. ve Yazıcı, A., 2009. Paralel Veri Madenciliği Algoritmaları . Ankara: I. Ulusal Yüksek Başarım ve Izgara Konferansı.
  • Foundation,What is R?, https://www.r-project.org/about.html,(Nowember 2019)
  • RapidMiner,Data Mining Tools, https://RapidMiner.com/glossary/data-mining-tools,(Nowember 2019).
  • Hofmann, M., & Klinkenberg, R. (Eds.). (2016). RapidMiner: Data mining use cases and business analytics applications. CRC Press.
  • Zhu, H. (1998). On-line analytical mining of association rules. Simon Fraser University.
  • Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (Eds.). (1996, February). Advances in knowledge discovery and data mining. American Association for Artificial Intelligence.
  • Srikant, R., & Agrawal, R. (1997). Mining generalized association rules. Future generation computer systems, 13(2-3), 161-180.
  • Han, Jiawei, And Yongjian Fu. "Discovery of multiple-level association rules from large databases." VLDB. Vol. 95. 1995.
  • Borgelt, C., & Kruse, R. (2002). Induction of association rules: Apriori implementation. In Compstat (pp. 395-400). Physica, Heidelberg.
  • J. Han, J. Pei, And Y Yin. "Mining frequent patterns without candidate generation." ACM sigmod record 29.2 (2000):1-12.
  • Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
  • Qiu, Y., Lan, Y. J., & Xie, Q. S. (2004, August). An improved algorithm of mining from FP-tree. In Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826) (Vol. 3, pp. 1665- 1670). IEEE.
  • Liu, Y., Gan, Z., & Sun, Y. (2008, August). Static hand gesture recognition and its application based on support vector machines. In 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (pp. 517-521). IEEE.

CRIME DATA ANALYSIS WITH ASSOCIATION RULE MINING

Year 2020, Volume: 2 Issue: 2, 42 - 50, 25.06.2021

Abstract

Along with the positive developments of the globalizing world, new types of crime such as social media fraud, drug trafficking and vehicle robbery, which have disrupted community welfare and order, have also emerged. With developments in information technology, it is possible to record real-time various data related to subject of crimes, location and time information, type of crime. By analyzing these recorded raw data using various data mining methods, it is possible to extract information that can be used to identify the data or for prediction purposes. In this study, an analysis of the association rules on the NIBRS Crime dataset which includes real crime cases from July 2016 to April 2018 in the state of Maryland in USA was carried out using R program with Apriori algorithm and RapidMiner with FP-Growth algorithm. With these association rules created, the time intervals, the districts, the types of crimes and the frequency of the occurrences are analyzed and the results of the algorithms are presented. With the results of this analysis; for organizations which are responsible for maintaining the peace and social order, such as security forces and law enforcement agencies; it is possible to follow useful information such as which crimes are committed more frequently and in which time period of day the criminals are more active.

References

  • Benton, W. " Encyclopedia Britannica". Encyclopedia Britannica Inc, Vol. 1,1971.
  • Yu, C. H., Ward, M. W., Morabito, M., & Ding, W. (2011, December). Crime forecasting using data mining techniques. In 2011 IEEE 11th international conference on data mining workshops (pp. 779-786). IEEE.
  • Merriam-Webster-Dictionary, , https://www.merriam-webster.com/dictionary ,(09,2019).
  • Takçı, H. ve Hayta, Ş., 2014. Suç Veri Madenciliği Yardımıyla Hırsızlık Suçları Hakkında Kural Çıkarımı. İçinde: Elektrik-Elektronik Ve Biyomedikal Mühendisliği Konferansi . Bursa: ELECO, s.694-699.
  • Brown, D. E. (1998, October). The regional crime analysis program (RECAP): a framework for mining data to catch criminals. In SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 98CH36218) (Vol. 3, pp. 2848-2853). IEEE.
  • Turvey, B. (2002). Inductive criminal profiling. In Criminal Profiling: An Introduction to Behavioral Evidence Analysis (pp. 21-33). Academic Press London.
  • Data.Gov: Montgomery County-America ,Crime, data.montgomerycountymd.gov,(March 2018).
  • Ünsal, Ö. (2011). Mesleki Alan Seçimlerinin Makine Öğrenmesi Algoritması Kullanılara Belirlenmesi (Doctoral dissertation, Yüksek Lisans Tezi, Gazi Üniversitesi, Bilişim Enstitüsü, Ankara).
  • Özkan, Y. (2008). Veri madenciliği yöntemleri. Papatya Yayıncılık Eğitim. Ergün, K. (n.d.). Veri Madenciliğine Giriş. Balıkesir Üniversitesi Mühendislik Fakültesi Endüstri Mühendisliği Bölümü., from http://kergun.baun.edu.tr/veri_madenciligi_ceng_hafta1.pdf,(June 2019)
  • Chen, H., Chung, W., Qin, Y., Chau, M., Xu, J. J., Wang, G., ... & Atabakhsh, H. (2003, May). Crime data mining: an overview and case studies. In Proceedings of the 2003 annual national conference on Digital government research (pp. 1-5).
  • Ozgul, F., Atzenbeck, C., Celik, A., & Erdem, Z. (2011, July). Incorporating data sources and methodologies for crime data mining. In Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics (pp. 176-180). IEEE.
  • Mason, S. J. (2019). Analysis of Virginia Crime Data of the Year 2016 Using Data Mining Techniques (Doctoral dissertation, North Carolina Agricultural and Technical State University).
  • Mittal, M., Goyal, L. M., Sethi, J. K., & Hemanth, D. J. (2019). Monitoring the impact of economic crisis on crime in India using machine learning. Computational Economics, 53(4), 1467-1485.
  • Thangamuthu, M. A., Vadivel, M. G., & Priyadharshini, M. A. (2019). Detecting Criminal Method using Data Mining.
  • Ma, L., Chen, Y., & Huang, H. (2010, November). AK-Modes: A weighted clustering algorithm for finding similar case subsets. In 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering (pp. 218-223). IEEE.
  • Agrawal, R., & Shafer, J. C. (1996). Parallel mining of association rules. IEEE Transactions on knowledge and Data Engineering, 8(6), 962-969.
  • Agrawal, R., Imieliński, T., & Swami, A. (1993, June). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD international conference on Management of data (pp. 207-216).
  • Agarwal, R., & Srikant, R. (1994, September). Fast algorithms for mining association rules. In Proc. of the 20th VLDB Conference (pp. 487-499).
  • Şeker, S. E. (n.d.). Apriori Algoritması. Bilgisayar Kavramlari, http://bilgisayarkavramlari.sadievrenseker.com/2011/09/07/apriori-algoritmasi/,(May 2019)
  • Zhao, Q., & Bhowmick, S. S. (2003). Association rule mining: A survey. Nanyang Technological University, Singapore, 135.
  • Alataş, B.(2003). Nicel Birliktelik Kurallarının Keşfinde Bulanık Mantık ve Genetik Algoritma Yaklaşımı.Yüksek Lisans Tezi, Fırat Üniversitesi, Elazığ, Türkiye.
  • Hasan, M.,Apriori Algorithm , https://www.slideshare.net/mainul_hs/apriori-algorithm- 44679525,(Nowember 2019)
  • Chanda, AK., Apriori Algorith https://www.slideshare.net/ashisface/apriori-algorithm- 34707400,(Nowember 2019)
  • Alataş, B., and Arslan, A., Mining of Fuzzy Association Rules with Genetic Algorithms, Journal of Polytechnic, Vol: 7, No: 4 pp. 269-276, 2004.
  • Birant, D., Kut, A., Ventura, M., Altınok, H., Altınok, B., Altınok, E., & Ihlamur, M. (2010). İş Zekası Çözümleri için Çok Boyutlu Birliktelik Kuralları Analizi. Akademik Bilişim, 10, 256.
  • Öğütücü, ŞG.,Veri Madenciliği İlişkilendirme Kuralları,https://web.itu.edu.tr/~sgunduz/courses/verimaden/slides/d7.pdf,(Nowember 2019)
  • Ödoğan, G., Abul, O. ve Yazıcı, A., 2009. Paralel Veri Madenciliği Algoritmaları . Ankara: I. Ulusal Yüksek Başarım ve Izgara Konferansı.
  • Foundation,What is R?, https://www.r-project.org/about.html,(Nowember 2019)
  • RapidMiner,Data Mining Tools, https://RapidMiner.com/glossary/data-mining-tools,(Nowember 2019).
  • Hofmann, M., & Klinkenberg, R. (Eds.). (2016). RapidMiner: Data mining use cases and business analytics applications. CRC Press.
  • Zhu, H. (1998). On-line analytical mining of association rules. Simon Fraser University.
  • Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (Eds.). (1996, February). Advances in knowledge discovery and data mining. American Association for Artificial Intelligence.
  • Srikant, R., & Agrawal, R. (1997). Mining generalized association rules. Future generation computer systems, 13(2-3), 161-180.
  • Han, Jiawei, And Yongjian Fu. "Discovery of multiple-level association rules from large databases." VLDB. Vol. 95. 1995.
  • Borgelt, C., & Kruse, R. (2002). Induction of association rules: Apriori implementation. In Compstat (pp. 395-400). Physica, Heidelberg.
  • J. Han, J. Pei, And Y Yin. "Mining frequent patterns without candidate generation." ACM sigmod record 29.2 (2000):1-12.
  • Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
  • Qiu, Y., Lan, Y. J., & Xie, Q. S. (2004, August). An improved algorithm of mining from FP-tree. In Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826) (Vol. 3, pp. 1665- 1670). IEEE.
  • Liu, Y., Gan, Z., & Sun, Y. (2008, August). Static hand gesture recognition and its application based on support vector machines. In 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (pp. 517-521). IEEE.
There are 39 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Duygu Çalışkan 0000-0003-4523-2967

Kazım Yıldız 0000-0001-6999-1410

Buket Doğan 0000-0003-1062-2439

Abdulsamet Aktaş 0000-0003-0746-7693

Publication Date June 25, 2021
Published in Issue Year 2020 Volume: 2 Issue: 2

Cite

APA Çalışkan, D., Yıldız, K., Doğan, B., Aktaş, A. (2021). CRIME DATA ANALYSIS WITH ASSOCIATION RULE MINING. International Periodical of Recent Technologies in Applied Engineering, 2(2), 42-50.
AMA Çalışkan D, Yıldız K, Doğan B, Aktaş A. CRIME DATA ANALYSIS WITH ASSOCIATION RULE MINING. PORTA. June 2021;2(2):42-50.
Chicago Çalışkan, Duygu, Kazım Yıldız, Buket Doğan, and Abdulsamet Aktaş. “CRIME DATA ANALYSIS WITH ASSOCIATION RULE MINING”. International Periodical of Recent Technologies in Applied Engineering 2, no. 2 (June 2021): 42-50.
EndNote Çalışkan D, Yıldız K, Doğan B, Aktaş A (June 1, 2021) CRIME DATA ANALYSIS WITH ASSOCIATION RULE MINING. International Periodical of Recent Technologies in Applied Engineering 2 2 42–50.
IEEE D. Çalışkan, K. Yıldız, B. Doğan, and A. Aktaş, “CRIME DATA ANALYSIS WITH ASSOCIATION RULE MINING”, PORTA, vol. 2, no. 2, pp. 42–50, 2021.
ISNAD Çalışkan, Duygu et al. “CRIME DATA ANALYSIS WITH ASSOCIATION RULE MINING”. International Periodical of Recent Technologies in Applied Engineering 2/2 (June 2021), 42-50.
JAMA Çalışkan D, Yıldız K, Doğan B, Aktaş A. CRIME DATA ANALYSIS WITH ASSOCIATION RULE MINING. PORTA. 2021;2:42–50.
MLA Çalışkan, Duygu et al. “CRIME DATA ANALYSIS WITH ASSOCIATION RULE MINING”. International Periodical of Recent Technologies in Applied Engineering, vol. 2, no. 2, 2021, pp. 42-50.
Vancouver Çalışkan D, Yıldız K, Doğan B, Aktaş A. CRIME DATA ANALYSIS WITH ASSOCIATION RULE MINING. PORTA. 2021;2(2):42-50.

International Periodical of Recent Technologies in Applied Engineering