Summary of Empirical and Experimental Insights Into Data Mining Techniques For Crime Prediction: a Comprehensive Survey, by Kamal Taha
Empirical and Experimental Insights into Data Mining Techniques for Crime Prediction: A Comprehensive Survey
by Kamal Taha
First submitted to arxiv on: 17 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The survey paper presents a comprehensive analysis of crime prediction methodologies, exploring various statistical methods, machine learning algorithms, and deep learning techniques used to analyze crime data. The paper proposes a methodological taxonomy that classifies crime prediction algorithms into specific techniques, with four tiers: methodology category, sub-category, technique, and sub-technique. Empirical and experimental evaluations are provided to rank the different techniques based on criteria such as accuracy, precision, recall, and F1 score. The study aims to provide a nuanced understanding of crime prediction algorithms, aiding researchers in making informed decisions. Key contributions include the proposed taxonomy, empirical and experimental evaluations, and insights into future advancements and opportunities for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Crime prediction is a growing field that uses data analysis to forecast where crimes might happen. This paper looks at different ways people are trying to do this, like using statistics or machine learning algorithms. The researchers propose a way to group these methods together based on what they’re doing and how well they work. They tested the methods by looking at things like accuracy and precision. This helps people working in crime prediction make good decisions about which method to use. The paper also gives some ideas for new ways to improve crime prediction in the future. |
Keywords
* Artificial intelligence * Deep learning * F1 score * Machine learning * Precision * Recall
