Şehirlerin Suç Türlerine Göre İkili Kümeleme Yöntemi ile Gruplandırılması: Türkiye Örneği
Year 2018,
Volume: 22 Issue: Special, 110 - 120, 05.10.2018
H. Hasan Örkcü
,
Bülent Altunkaynak
Ramazan Arslan
Abstract
Suç bölgelerinin oluşturulması suçlara karşı önlemlerin alınmasında kritik öneme sahiptir. Bu bölgelerin oluşumunda kullanılan geleneksel kümeleme yöntemleri yalnızca tek boyutta kümeleme yaparken, belirli kümeler yerine genel sonuçlar sağlar. Bu çalışmada, ayrıntılı kümelerin oluşturulması için ikili kümeleme (biclustering) yöntemlerinden Bimax algoritmasının uygulanabileceği önerilmektedir. Bu yöntemle, hem suçun işlendiği bölgeler hem de suç türleri aynı anda kümelenerek suç bölgeleri oluşturulmuştur. Bu suç bölgeleri ile ilgili sosyo-ekonomik değişkenler arasındaki farklılıklar analiz edilmiş ve suç bölgelerine özgü özellikler sunulmuştur.
References
- [1] Brown D.E. 1998. The Regional Crime Analysis Program (RECAP): A framework for mining data to catch criminals, IEEE, 2848-2853.
- [2] Adderly, R., Musgrove, B.P. 2001. Data mining case study: Modeling the behavior of offenders who commit serious sexual assaults. KDD-‘01, Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 215-220.
- [3] Bruin, J.S., Cocx, T.K., Kosters, W.A., Laros,J., Kok, J.N. 2006. Data mining approaches to criminal career analysis. In Proceedings of the Sixth International Conference on Data Mining (ICDM’) (ICDM’06), 171-177.
- [4] 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.
- [5] Oguzlar, A. 2005. A new approach to clustering analysis: Self-organizing maps. Ataturk University Journal of Economics and Administrative Sciences, 19 (2).
- [6] Tuzunturk, S. 2009. Multidimensional Scaling Analysis: An application on crime statistics, Uludag University, Journal of Economics and Administrative Sciences, 28 (2), 71-91.
- [7] Cömertler, N., Kar, M. 2010. Economic and Social Determinants of the Crime Rate in Turkey: Cross-Section Analysis. Ankara University SBF Journal, 62-2.
- [8] Ma, L., Chen Y., Huang, H. 2010. AK-Modes: A weighted clustering algorithm for finding similar case subsets. IEEE, 218-223.
- [9] Izenman, A.J., Harris, P.W., Mennis, J., Jupin, J., Obradovic, Z. 2011. Local Spatial Biclustering and Prediction of Urban Juvenile Delinquency and Recidivism, Statistical Analysis and Data Mining, 4, 259-275.
- [10] Huang, Q., Yang, F., Liu, L. Li, X. 2015a. Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis, Information Sciences, 314, 293–310.
- [11] Cheng, Y., Church G.M. 2000. Biclustering of expression data. Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology 1, 93-103.
- [12] Govaert, G., Nadif, M. 2008. Block clustering with Bernoulli mixture models: Comparison of different approaches. Computational Statistics and Data Analysis, 52 (6), 3233–3245.
- [13] Govaert, G., Nadif, M. 2013. Co-clustering: Models, algorithms and applications. ISTE, Wiley.
- [14] Van Mechelen I, Bock H.H., De Boeck P. 2004. Two-mode clustering methods: A structured overview, Statistical Methods in Medical Research, 13 (5), 363–94.
- [15] Hartigan J. A. 1972. Direct clustering of a data matrix. Journal of the American Statistical Association (JASA). 67(337), 123–129.
- [16] Zhao, H., Liew, A.W.C., Xie, X., 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.
- [17] Zhao, H., Chan, K.L., Cheng, L.M., Hong, Y. 2009. A probabilistic relaxation labeling framework for reducing the noise effect in geometric biclustering of gene expression data, Pattern Recognite, 42 (11), 2578–2588.
- [18] Prelic, A., Bleuler, S., Zimmermann, P., Wil, A., Buhlmann, P., Gruissem, W., Hennig, L., Thiele, L., Zitzler, E. 2006. A systematic comparison and evaluation of biclustering methods for gene expression data bioinformatics, Oxford Univ. Press, 22, 1122-1129.
- [19] Lazzeroni L., Owen A. 2000. Plaid models for gene expression data. Technical Report, Stanford University.
- [20] Turner, H., Bailey, T., Krzanowski, W. 2003. Improved biclustering of microarray data demonstrated through systematic performance tests. Computational Statistics & Data Analysis 48 (2), 235-254.
- [21] Murali, T., Kasif S. 2003. Extracting conserved gene expression motifs from gene expression data. Pacic Symposium on Biocomputing 8, 77-88.
- [22] Kluger, Y., Basri, R.J.T., Chang, Gerstein M. 2003. Spectral biclustering of microarray data: Co-clustering genes and conditions, Genome Research 13, 703-716.
- [23] Wang, B., Miao Y., Zhao H., Jing J., Chen Y., 2016. A biclustering-based method for market segmentation using customer pain points, Engineering Applications of Artificial Intelligence 47, 101–109.
- [24] Madeira S.C, Oliveira A.L. 2004. Biclustering algorithms for biological data analysis: A survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics 1 (1), 24-45.
- [25] Hofmann T., Puzicha J. 1999. Latent class models for collaborative filtering, In Proceedings of the International Joint Conferenceon Artificial Intelligence. 668–693.
- [26] Huang, Q., Wang, T., Tao, D., Li, X., 2015b. Biclustering Learning of Trading Rules, IEEE Transactions on Cybernetics, 45 (10), 2287-2298.
- [27] Raponi V., Martella F., Maruotti A. 2016. A biclustering approach to university performances: An Italian case study, Journal of Applied Statistics, 43 (1), 31-45.
- [28] Verma, N.K., Meena, S., Singh, A., Cui, Y., Bajpai, S. Nagrare, A. 2010. A comparison of biclustering algorithms, Proceedings of 2010 Int. Conference on Systems in Medicine and Biology, 90-97, 16-18 December 2010, IIT Kharagpur, India.
- [29] Troyer, E.D., Sanden, S., Shkedy, Z., Kaiser, S. 2017. Bimax Algorithm. In Talloen, W. (Ed.), Applied Biclustering Methods for Big and High-Dimensional Data Using R, 61-67, Chapman&Hall, New York.
Year 2018,
Volume: 22 Issue: Special, 110 - 120, 05.10.2018
H. Hasan Örkcü
,
Bülent Altunkaynak
Ramazan Arslan
References
- [1] Brown D.E. 1998. The Regional Crime Analysis Program (RECAP): A framework for mining data to catch criminals, IEEE, 2848-2853.
- [2] Adderly, R., Musgrove, B.P. 2001. Data mining case study: Modeling the behavior of offenders who commit serious sexual assaults. KDD-‘01, Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 215-220.
- [3] Bruin, J.S., Cocx, T.K., Kosters, W.A., Laros,J., Kok, J.N. 2006. Data mining approaches to criminal career analysis. In Proceedings of the Sixth International Conference on Data Mining (ICDM’) (ICDM’06), 171-177.
- [4] 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.
- [5] Oguzlar, A. 2005. A new approach to clustering analysis: Self-organizing maps. Ataturk University Journal of Economics and Administrative Sciences, 19 (2).
- [6] Tuzunturk, S. 2009. Multidimensional Scaling Analysis: An application on crime statistics, Uludag University, Journal of Economics and Administrative Sciences, 28 (2), 71-91.
- [7] Cömertler, N., Kar, M. 2010. Economic and Social Determinants of the Crime Rate in Turkey: Cross-Section Analysis. Ankara University SBF Journal, 62-2.
- [8] Ma, L., Chen Y., Huang, H. 2010. AK-Modes: A weighted clustering algorithm for finding similar case subsets. IEEE, 218-223.
- [9] Izenman, A.J., Harris, P.W., Mennis, J., Jupin, J., Obradovic, Z. 2011. Local Spatial Biclustering and Prediction of Urban Juvenile Delinquency and Recidivism, Statistical Analysis and Data Mining, 4, 259-275.
- [10] Huang, Q., Yang, F., Liu, L. Li, X. 2015a. Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis, Information Sciences, 314, 293–310.
- [11] Cheng, Y., Church G.M. 2000. Biclustering of expression data. Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology 1, 93-103.
- [12] Govaert, G., Nadif, M. 2008. Block clustering with Bernoulli mixture models: Comparison of different approaches. Computational Statistics and Data Analysis, 52 (6), 3233–3245.
- [13] Govaert, G., Nadif, M. 2013. Co-clustering: Models, algorithms and applications. ISTE, Wiley.
- [14] Van Mechelen I, Bock H.H., De Boeck P. 2004. Two-mode clustering methods: A structured overview, Statistical Methods in Medical Research, 13 (5), 363–94.
- [15] Hartigan J. A. 1972. Direct clustering of a data matrix. Journal of the American Statistical Association (JASA). 67(337), 123–129.
- [16] Zhao, H., Liew, A.W.C., Xie, X., 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.
- [17] Zhao, H., Chan, K.L., Cheng, L.M., Hong, Y. 2009. A probabilistic relaxation labeling framework for reducing the noise effect in geometric biclustering of gene expression data, Pattern Recognite, 42 (11), 2578–2588.
- [18] Prelic, A., Bleuler, S., Zimmermann, P., Wil, A., Buhlmann, P., Gruissem, W., Hennig, L., Thiele, L., Zitzler, E. 2006. A systematic comparison and evaluation of biclustering methods for gene expression data bioinformatics, Oxford Univ. Press, 22, 1122-1129.
- [19] Lazzeroni L., Owen A. 2000. Plaid models for gene expression data. Technical Report, Stanford University.
- [20] Turner, H., Bailey, T., Krzanowski, W. 2003. Improved biclustering of microarray data demonstrated through systematic performance tests. Computational Statistics & Data Analysis 48 (2), 235-254.
- [21] Murali, T., Kasif S. 2003. Extracting conserved gene expression motifs from gene expression data. Pacic Symposium on Biocomputing 8, 77-88.
- [22] Kluger, Y., Basri, R.J.T., Chang, Gerstein M. 2003. Spectral biclustering of microarray data: Co-clustering genes and conditions, Genome Research 13, 703-716.
- [23] Wang, B., Miao Y., Zhao H., Jing J., Chen Y., 2016. A biclustering-based method for market segmentation using customer pain points, Engineering Applications of Artificial Intelligence 47, 101–109.
- [24] Madeira S.C, Oliveira A.L. 2004. Biclustering algorithms for biological data analysis: A survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics 1 (1), 24-45.
- [25] Hofmann T., Puzicha J. 1999. Latent class models for collaborative filtering, In Proceedings of the International Joint Conferenceon Artificial Intelligence. 668–693.
- [26] Huang, Q., Wang, T., Tao, D., Li, X., 2015b. Biclustering Learning of Trading Rules, IEEE Transactions on Cybernetics, 45 (10), 2287-2298.
- [27] Raponi V., Martella F., Maruotti A. 2016. A biclustering approach to university performances: An Italian case study, Journal of Applied Statistics, 43 (1), 31-45.
- [28] Verma, N.K., Meena, S., Singh, A., Cui, Y., Bajpai, S. Nagrare, A. 2010. A comparison of biclustering algorithms, Proceedings of 2010 Int. Conference on Systems in Medicine and Biology, 90-97, 16-18 December 2010, IIT Kharagpur, India.
- [29] Troyer, E.D., Sanden, S., Shkedy, Z., Kaiser, S. 2017. Bimax Algorithm. In Talloen, W. (Ed.), Applied Biclustering Methods for Big and High-Dimensional Data Using R, 61-67, Chapman&Hall, New York.