Analysis and Identification of Forensic Events Using Non-parametric Density Estimation

Author:

Kowalski Piotr A.ORCID,Kocierz Karol,Kusy MaciejORCID

Publisher

Springer International Publishing

Reference10 articles.

1. AlJanabi, K.B.S., Haydar, K.: Crime Data Analysis Using Data Mining Techniques To Improve Crimes Prevention Procedures. ICIT (2010)

2. Baltimore-DataSet: The Baltimore City Office of Information and Technology (BCIT), BPD Part 1 Victim Based Crime Data, Dec. 12 2020. https://data.baltimorecity.gov/Public-Safety/BPD-Part-1-Victim-Based-Crime-Data/wsfq-mvij

3. Bortoloti, F.D., de Oliveira, E., Ciarelli, P.M.: Supervised kernel density estimation k-means. Expert Systems with Applications, p. 114350 (2020). https://doi.org/10.1016/j.eswa.2020.114350, http://www.sciencedirect.com/science/article/pii/S0957417420310344

4. Kowalski, P.A., Kulczycki, P.: A complete algorithm for the reduction of pattern data in the classification of interval information. Int. J. Comput. Methods 13(03), 1650018 (2016)

5. Kulczycki, P.: Applicational possibilities of nonparametric estimation of distribution density for control engineering. Bulletin of the Polish Academy of Sciences: Technical Sciences, pp. 347–359 (2008)

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