Abstract

A data preprocessing is a process of cleaning the data, data integration and data transformation. It intends to reduce some noises and inconsistent data. Data preprocessing is the process of keeping the dataset ready for the process. The results of preprocessing step are later used by data mining algorithms. This paper focus on preprocessing the attributes that are related to crime data and that affects the final output of the mining processes.

References

• Dempster, A. P. , Laird, N. M. and Rubin, D. B. (1977) Maximum likelihood for incomplete data via the EM algorithm. J. R. Stat. Soc. , Ser. B, Vol. 39, Pp. 1–38.
• Duda, R. O. , Hart, P. E. and Stork, D. G. (2000) Pattern Classification, Wiley-Interscience, New York.
• Hammer, B. and Villmann, T. (2002) Generalized relevance learning vector quantization, Neural Networks, Vol. 15, Issues 8–9, Pp. 1059–1068.
• Kapur, J. N. (1994) Measures of Information and their Application, Wiley, New Delhi.
• Kohonen, T. (1997) Self-Organizing Maps, vol. 30 of Springer Series in Information Sciences, Springer, Berlin, Heidelberg, 1995, (Second Extended Edition 1997).
• Kwak, N. and Choi, C. H. (2002) Input feature selection by mutual information based on Parzen window, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 12, Pp. 1667–1671.
• Meyering, A. and Ritter, H. (1992) Learning 3D-shape-perception with local linear maps, Proceedings of IJCNN'92, Pp. 432. 436.
• Martínez-Muñoz, G. and Suárez, A. (2004), Using all Data to Generate Decision Tree Ensemble, IEEE Tran. On Systems, Man and Cybernetics—part C: Applications and Review, Vol. 34, No. 4, Pp. 393-397.
• Peres, R. T. and Pedreira, C. E. (2009) Generalized Risk Zone: Selecting Observations for Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 7, Pp. 1331-1337.
• Pregenzer, M. , Pfurtscheller, G. and Flotzinger, D. (1996) Automated feature selection with distinction sensitive learning vector quantization. Neurocomputing, Vol. 11, Pp. 19-29.
• Sato, A. and Yamada, K. (1998) A formulation of learning vector quantization using a new misclassification measure, Proceedings of Fourteenth International Conference on Pattern Recognition, A. K. Jain, S. Venkatesh, and B. C. Lovell, Eds. , IEEE Computer Society, Los Alamitos, CA, USA, Vol. 1, Pp. 322-325.
• Sebastiani, F. (2002) Machine learning in automated text categorization, ACM Computing Surveys, Vol. 34, No. 1, Pp. 1- 47.
• Villmann, Th. and Hammer, B. (2002) Supervised neural gas for learning vector quantization, Proc. of the 5th German Workshop on Artificial Life (GWAL-5), D. Polani, J. Kim, and T. Martinetz, Eds. ,Akademische Verlagsgesellschaft - infix - IOS Press, Berlin, Pp. 9-16.
• Villmann, Th. , Schleif, F. and Hammer, B. (2006) Comparison of Relevance Learning Vector Quantization with other Metric Adaptive Classification Methods, Neural Networks, Vol. 19, Issue 5, Pp. 610-622
• Zhang, H. (2004) The optimality of Naive Bayes, American Association for Artificial Intelligence, Flairs Conference, Pp. 257-319
• Adèr, H. J. and Mellenbergh, G. J. (Eds. ) (2008) Chapter 13: Missing data, Advising on Research Methods: A consultant's companion, Huizen, The Netherlands: Johannes van Kessel Publishing, Pp. 305-332.
• Chen, N. , Vieira, A. , Ribeiro, B. , Duarte, J. and Neves, J (2011) A stable credit rating model based on learning vector quantization, Vol. 15, No. 2/2011, Intelligent Data Analysis, Pp. 237-250