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CLUSTERING OF CRIMINALS ACCORDING TO THE TYPES OF MATERIAL CAUGHT IN SMUGGLING: BICLUSTERING METHOD

Year 2018, 18. EYI Special Issue, 883 - 896, 25.01.2018
https://doi.org/10.18092/ulikidince.348119

Abstract

Smuggling is a multidimensional, multi-actor and variable structure process
that negatively affects to our country in the economic and social aspects and
same time provides financial support for terror and criminal organizations to
carry on their activities. Investigation of criminal profiles in taking
measures against crimes also has a critical importance. The aim of this study
is to clusters of smuggling-related criminals according to the material they
are caught. In this way, clusters of related trafficking materials were
established and common characteristics of the criminals belonging to these
clusters were determined. In this study, the use of the bicluster method is
proposed as it is different from classical clustering methods because both
trafficking materials and criminals are clustered at the same time. Bimax
algorithm is used as binary clustering method. According to the results
obtained, the related materials were clustered and by bringing together the
criminals in these clusters, a number of characteristics related to the
criminal profiler were revealed.

References

  • Adalet Bakanlığı Adli Sicil ve İstatistik Genel Müdürlüğü, (2017). Haber Bülteni, Sayı:11.
  • Brown, D.E., (1998). The Regional Crime Analysis Program (RECAP): A Framework for Mining Data to Catch Criminals. IEEE, 2848-2853.
  • Bruin, J.S., Cocx, T.K., Kosters, W.A., Laros, J. ve Kok J.N., (2006). Data Mining Approaches to Crimi-nal Career Analysis. In Proceedings of the Sixth International Conference on Data Mining (ICDM’) (ICDM’06), 171-177.
  • Cheng, Y. ve Church, G.M., (2000). Biclustering of Expression Data. Proceedings of the Eighth In-ternational Conference on Intelligent Systems for Molecular Biology 1, 93-103.
  • Emniyet Genel Müdürlüğü KOM Daire Başkanlığı, (2015). 2014 Kaçakçılık ve Organize Suçlarla Mü-cadele Raporu, KOM Yayınları, Ankara.
  • Giray, S. (2016). İki Aşamalı Kümeleme Analizi ile Hükümlü Verilerinin İncelenmesi. Ekonometri ve İstatistik, 25, 1-31.
  • Govaert, G., ve Nadif, M., (2008). Block Clustering with Bernoulli Mixture Models: Comparison of Different Approaches. Computational Statistics and Data Analysis, 52 (6), 3233–3245.
  • Govaert, G. ve Nadif, M., (2013). Co-Clustering: Models, Algorithms and Applications. ISTE, Wiley.
  • Hartigan, J. A., (1972). Direct Clustering of a Data Matrix. Journal of the American Statistical Asso-ciation (JASA), 67(337), 123–129.
  • Hofmann, T. ve Puzicha, J., (1999). “Latent Class Models for Collaborative Filtering. In Proceedings of the International Joint Conferenceon Artificial Intelligence, 668–693.
  • Kluger Y., Basri R., Chang J.T. ve Gerstein M., (2003). Spectral Biclustering of Microarray Data: Co-Clustering Genes and Conditions. Genome Research 13, 703-716. Lazzeroni, L. ve Owen, A., (2000). Plaid Models for Gene Expression Data. Technical Report, Stan-ford University, 1-26.
  • Ma, L., Chen, Y. ve Huang, H., (2010). AK-Modes: A Weighted Clustering Algorithm for Finding Simi-lar Case Subsets. IEEE, 218-223.
  • Murali, T. ve Kasif, S., (2003). Extracting Conserved Gene Expression Motifs from Gene Expression Data. Pacic Symposium on Biocomputing 8, 77-88.
  • Nath, S.V., (2006). Crime Pattern Detection Using Data Mining. International Conferences on Web Intelligence and Intelligent Agent Technology-Workshops, IEEE/WIC/ACM, 41-44.
  • Oğuzlar, A., (2005). Kümeleme Analizinde Yeni Bir Yaklaşım: Kendini Düzenleyen Haritalar (Koho-nen Ağları). İktisadi ve İdari Bilimler Dergisi, 19 (2), 93-107.
  • Polat, C., Eren, H. ve Erbakıcı, F., (2013). Hırsızlık Suçunu Etkileyen Faktörlerin Değerlendirilmesi ve Geleceğe Yönelik Yaklaşımlar. Güvenlik Bilimleri Dergisi, 2(1), 1-33.
  • Prelic, A., Bleuler, S., Zimmermann, P., Wil, A., Buhlmann, P., Gruissem, W., Hennig, L., Thiele, L. ve Zitzler, E., (2006). A Systematic Comparison and Evaluation of Biclustering Methods for Gene Expression Data Bioinformatics. Oxford Univ. Press, 22, 1122-1129.
  • Raponi, V., Martella, F. ve Maruotti ,A., (2016). A Biclustering Approach to University Performan-ces: An Italian Case Study. Journal of Applied Statistics, 43 (1), 31-45. Reale, K., Beauregard, E. ve Martineau, M. (2017). Sadism in Sexual Homicide Offenders: İdentif-ying Distinct Groups. Journal of Criminal Psychology, 7(2), 120-133.
  • Sea, J., Kim, K. ve Youngs, D., (2016). Behavioural Profiles and Offender Characteristics Across 111 Korean Sexual Assults. Journal of Investigative Psychology and Offender Profiling, 13, 3-21.
  • Turner, H., Bailey, T. ve Krzanowski, W., (2003). Improved Biclustering of Microarray Data De-monstrated Through Systematic Performance Tests. Computational Statistics & Data Analysis, 48 (2) ,235-254.
  • Tüzüntürk, S., (2009). Çok Boyutlu Ölçekleme Analizi: Suç İstatistikleri Üzerine Bir Uygulama. Ulu-dağ Üniversitesi, İktisadi ve İdari Bilimler Fakültesi Dergisi 28 (2), 71-91.
  • Van Mechelen, I., Bock, H.H. ve De Boeck, P., (2004).Two-Mode Clustering Methods: A Structured Overview. Statistical Methods in Medical Research, 13 (5), 363–94.
  • Wang, B., Miao, Y., Zhao, H., Jing, J. ve Chen, Y., (2016). A Biclustering-Based Method for Market Segmentation Using Customer Pain Points. Engineering Applications of Artificial Intelli-gence, 47, 101–109.
  • Zhao, H., Liew, A.W.C., Xie, X., ve Yan, H., (2007). A New Geometric Biclustering Algorithm Based on the Hough Transform for Analysis of Large-Scale Microarray Data. J.Theor. Biol. 251, 264–74.
  • Zhao, H., Chan, K.L., Cheng, L.M., ve Hong, Y., (2009). A Probabilistic Relaxation Labeling Fra-mework for Reducing the Noise Effect in Geometric Biclustering of Gene Expression Data. Pattern Recognit, 42 (11), 2578–2588.

KAÇAKÇILIKTA YAKALANAN MALZEME TÜRLERİNE GÖRE SUÇLULARIN KÜMELENMESİ: İKİLİ KÜMELEME YÖNTEMİ

Year 2018, 18. EYI Special Issue, 883 - 896, 25.01.2018
https://doi.org/10.18092/ulikidince.348119

Abstract

Kaçakçılık, ülkemizi ekonomik ve sosyal yönden olumsuz etkileyen, aynı
zamanda terör ve suç örgütlerinin faaliyetlerini sürdürebilmesi için finansal
destek sağlayan çok boyutlu, çok aktörlü ve değişken yapılı bir süreçtir.
Suçlara karşı önlemlerin alınmasında suçlu profillerinin incelenmesi de ayrıca
kritik bir öneme sahiptir. Bu çalışmanın amacı kaçakçılıkla ilgili suçluların
yakalandıkları malzemelere göre kümelenmesidir. Bu sayede birbiriyle ilişkili
kaçakçılık malzemelerine ilişkin kümeler oluşturulmuş ve bu kümelere ait
suçluların ortak özellikleri belirlenmiştir. Bu çalışmada hem kaçakçılık
malzemeleri hem de suçlular aynı anda kümelendikleri için klasik kümeleme
yöntemlerinden farklı olarak ikili kümeleme yönteminin kullanımı
önerilmektedir. İkili kümeleme yöntemi olarak Bimax algoritması kullanılmıştır.
Elde edilen sonuçlara göre, birbiriyle ilişkili malzemeler kümelenmiş ve bu
kümelerde yer alan suçluların bir araya getirilmesiyle de suçlu profillerine
ilişkin bir takım özellikler ortaya çıkarılmıştır.

References

  • Adalet Bakanlığı Adli Sicil ve İstatistik Genel Müdürlüğü, (2017). Haber Bülteni, Sayı:11.
  • Brown, D.E., (1998). The Regional Crime Analysis Program (RECAP): A Framework for Mining Data to Catch Criminals. IEEE, 2848-2853.
  • Bruin, J.S., Cocx, T.K., Kosters, W.A., Laros, J. ve Kok J.N., (2006). Data Mining Approaches to Crimi-nal Career Analysis. In Proceedings of the Sixth International Conference on Data Mining (ICDM’) (ICDM’06), 171-177.
  • Cheng, Y. ve Church, G.M., (2000). Biclustering of Expression Data. Proceedings of the Eighth In-ternational Conference on Intelligent Systems for Molecular Biology 1, 93-103.
  • Emniyet Genel Müdürlüğü KOM Daire Başkanlığı, (2015). 2014 Kaçakçılık ve Organize Suçlarla Mü-cadele Raporu, KOM Yayınları, Ankara.
  • Giray, S. (2016). İki Aşamalı Kümeleme Analizi ile Hükümlü Verilerinin İncelenmesi. Ekonometri ve İstatistik, 25, 1-31.
  • Govaert, G., ve Nadif, M., (2008). Block Clustering with Bernoulli Mixture Models: Comparison of Different Approaches. Computational Statistics and Data Analysis, 52 (6), 3233–3245.
  • Govaert, G. ve Nadif, M., (2013). Co-Clustering: Models, Algorithms and Applications. ISTE, Wiley.
  • Hartigan, J. A., (1972). Direct Clustering of a Data Matrix. Journal of the American Statistical Asso-ciation (JASA), 67(337), 123–129.
  • Hofmann, T. ve Puzicha, J., (1999). “Latent Class Models for Collaborative Filtering. In Proceedings of the International Joint Conferenceon Artificial Intelligence, 668–693.
  • Kluger Y., Basri R., Chang J.T. ve Gerstein M., (2003). Spectral Biclustering of Microarray Data: Co-Clustering Genes and Conditions. Genome Research 13, 703-716. Lazzeroni, L. ve Owen, A., (2000). Plaid Models for Gene Expression Data. Technical Report, Stan-ford University, 1-26.
  • Ma, L., Chen, Y. ve Huang, H., (2010). AK-Modes: A Weighted Clustering Algorithm for Finding Simi-lar Case Subsets. IEEE, 218-223.
  • Murali, T. ve Kasif, S., (2003). Extracting Conserved Gene Expression Motifs from Gene Expression Data. Pacic Symposium on Biocomputing 8, 77-88.
  • Nath, S.V., (2006). Crime Pattern Detection Using Data Mining. International Conferences on Web Intelligence and Intelligent Agent Technology-Workshops, IEEE/WIC/ACM, 41-44.
  • Oğuzlar, A., (2005). Kümeleme Analizinde Yeni Bir Yaklaşım: Kendini Düzenleyen Haritalar (Koho-nen Ağları). İktisadi ve İdari Bilimler Dergisi, 19 (2), 93-107.
  • Polat, C., Eren, H. ve Erbakıcı, F., (2013). Hırsızlık Suçunu Etkileyen Faktörlerin Değerlendirilmesi ve Geleceğe Yönelik Yaklaşımlar. Güvenlik Bilimleri Dergisi, 2(1), 1-33.
  • Prelic, A., Bleuler, S., Zimmermann, P., Wil, A., Buhlmann, P., Gruissem, W., Hennig, L., Thiele, L. ve Zitzler, E., (2006). A Systematic Comparison and Evaluation of Biclustering Methods for Gene Expression Data Bioinformatics. Oxford Univ. Press, 22, 1122-1129.
  • Raponi, V., Martella, F. ve Maruotti ,A., (2016). A Biclustering Approach to University Performan-ces: An Italian Case Study. Journal of Applied Statistics, 43 (1), 31-45. Reale, K., Beauregard, E. ve Martineau, M. (2017). Sadism in Sexual Homicide Offenders: İdentif-ying Distinct Groups. Journal of Criminal Psychology, 7(2), 120-133.
  • Sea, J., Kim, K. ve Youngs, D., (2016). Behavioural Profiles and Offender Characteristics Across 111 Korean Sexual Assults. Journal of Investigative Psychology and Offender Profiling, 13, 3-21.
  • Turner, H., Bailey, T. ve Krzanowski, W., (2003). Improved Biclustering of Microarray Data De-monstrated Through Systematic Performance Tests. Computational Statistics & Data Analysis, 48 (2) ,235-254.
  • Tüzüntürk, S., (2009). Çok Boyutlu Ölçekleme Analizi: Suç İstatistikleri Üzerine Bir Uygulama. Ulu-dağ Üniversitesi, İktisadi ve İdari Bilimler Fakültesi Dergisi 28 (2), 71-91.
  • Van Mechelen, I., Bock, H.H. ve De Boeck, P., (2004).Two-Mode Clustering Methods: A Structured Overview. Statistical Methods in Medical Research, 13 (5), 363–94.
  • Wang, B., Miao, Y., Zhao, H., Jing, J. ve Chen, Y., (2016). A Biclustering-Based Method for Market Segmentation Using Customer Pain Points. Engineering Applications of Artificial Intelli-gence, 47, 101–109.
  • Zhao, H., Liew, A.W.C., Xie, X., ve Yan, H., (2007). A New Geometric Biclustering Algorithm Based on the Hough Transform for Analysis of Large-Scale Microarray Data. J.Theor. Biol. 251, 264–74.
  • Zhao, H., Chan, K.L., Cheng, L.M., ve Hong, Y., (2009). A Probabilistic Relaxation Labeling Fra-mework for Reducing the Noise Effect in Geometric Biclustering of Gene Expression Data. Pattern Recognit, 42 (11), 2578–2588.
There are 25 citations in total.

Details

Journal Section Articles
Authors

Ramazan Arslan

Hacı Hasan Örkcü

Bülent Altunkaynak

Publication Date January 25, 2018
Published in Issue Year 2018 18. EYI Special Issue

Cite

APA Arslan, R., Örkcü, H. H., & Altunkaynak, B. (2018). KAÇAKÇILIKTA YAKALANAN MALZEME TÜRLERİNE GÖRE SUÇLULARIN KÜMELENMESİ: İKİLİ KÜMELEME YÖNTEMİ. Uluslararası İktisadi Ve İdari İncelemeler Dergisi883-896. https://doi.org/10.18092/ulikidince.348119

Cited By

Detection of Crime Regions with Biclustering Approach and Comparison of Methods
Sakarya University Journal of Computer and Information Sciences
İbrahim ÇİL
https://doi.org/10.35377/saucis.02.03.648342

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