Chapagain et al. (2022CHAPAGAIN P, TIMALSINA A, BHANDARI M & CHITRAKAR R. 2022. Intrusion Detection Based on PCA with Improved K-means. ICEEE International Conference on Electrical and Electronics Engineering, 894: 13-27.) |
In this study, the clustering approach based on PCA with k-means is proposed for intrusion detection in a system that detects malicious activities and issues alerts. |
Das & Das (2019DAS P & DAS AK. 2019. Graph-based clustering of extracted paraphrases for labelling crime reports. Knowledge-Based Systems, 179: 55-76.) |
The authors propose a grouping technique based on graphs to discover crime report labels based on paraphrases extracted from large bodies of unidentified crimes. |
Das et al. (2019DAS P, DAS AK, NAYAK J, PELUSI D & DING W. 2019. Group incremental adaptive clustering based on neural network and rough set theory or crime report categorization. Neurocomputing, doi: https://doi.org/10.1016/j.neucom.2019.10.109. https://doi.org/https://doi.org/10.1016/...
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The authors propose an incremental adaptive methodology in a crime reporting group, adapted to provide updated cluster sets. In addition, the study makes comparisons with clustering algorithms to express their effectiveness and statistical significance. |
Khan et al. (2019KHAN JR, SAEED M, SIDDIQUI FA, MAHMOOD N & UL ARIFEEN Q. 2019. Predictive Policing: A Machine Learning Approach to Predict and Control Crimes in Metropolitan Cities, Journal of Information in Communication Technology,3(1): 17-26.) |
The authors propose a model for the predictive policing system for street crimes in the Karachi-Pakistan region using the k-means tool and Bayesian methods. |
Farias et al. (2018FARIAS AMG, CINTRA ME & FELIX AC. 2018. Definition of Strategies for Crime Prevention and Combat Using Fuzzy Clustering and Formal Concept Analysis. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems , 26(3): 429-452.) |
This work presents different models related to public security. These models are based on clustering algorithms, on the analysis of formal concept techniques and on the analysis of crime record data collected in Mossoro, Rio Grande do Norte, Brazil. |
Wang et al (2018WANG S, WANG X, YE P, YUAN Y, LIU S & WANG F. 2018. Parallel Crime Scene Analysis Based on ACP Approach. IEEE Transactions on Computational Social Systems, 5(1): 244-255.) |
They propose a method of tied clues to obtain refined results regarding the phenomenon known in the analysis of crimes as the quasi-repetition effect, adopting a data science perspective, combining correlation coefficient, hierarchical cluster and mining of frequency patterns in a specific order. |
Saltos & Cocea (2017SALTOS G & COCEA M. 2017. An Exploration of Crime Prediction Using Data Mining on Open Data. International Journal of Information Technology & Decision Making , 16(5): 1155-1181.) |
In this paper, models to predict the frequency of several types of crimes by the LSOA code (an administrative system of areas used by the police in the UK) and the frequency of crimes related to anti-social behavior are presented. Three algorithms are used from different categories of approaches: instance-based learning, regression and decision trees. |
Borg & Boldt (2016)BOLT & BOLDT M. 2016. Clustering Residential Burglaries Using Modus Operandi and Spatiotemporal Information. International Journal of Information Technology & Decision Making, 15(1): 23-42.
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The study investigates the use of clustering algorithms to group similar crime reports based on combined characteristics from the structured form. |
Curman et al. (2015CURMAN ASN, ANDRESEN MA & BRANTINGHAM PJ. 2015. Crime and Place: A Longitudinal Examination of Street Segment Patterns in Vancouver. BC. Journal of Quantitative Criminology, 31: 127-147.) |
The authors conduct a longitudinal analysis of a 16-year data set using the street segment as the unit of analysis, highlighting the trends in the crimes, and using the group-based trajectory model with the k-means cluster analysis technique. |
Tayal et al. (2014TAYAL DK, JAIN A, ARORA S, AGARWAL S, GUPTA T & TYAGI N. 2014. Crime detection and criminal identification in India using data mining techniques. AI & Society, 30(1): 117-127.) |
The authors propose an approach for the design and implementation of crime detection and criminal identification for Indian cities using data mining techniques. |