A Decision Tree-Based Classification Model for Crime Prediction

Crime continues to remain a severe threat to all communities and nations across the globe alongside the sophistication in technology and processes that are being exploited to enable highly complex criminal activities. Data mining, the process of uncovering hidden information from Big Data, is now an important tool for investigating, curbing and preventing crime and is exploited by both private and government institutions around the world. The primary aim of this paper is to provide a concise review of the data mining applications in crime. To this end, the paper reviews over 100 applications of data mining in crime, covering a substantial quantity of research to date, presented in chronological order with an overview table of many important data mining applications in the crime domain as a reference directory. The data mining techniques themselves are briefly introduced to the reader and these include entity extraction, clustering, association rule mining, decision trees, support vector machines, naive Bayes rule, neural networks and social network analysis amongst others

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“…In ref. [55], a decision treebased classification model is used for discovering crime patterns and predicting future trends. Recently, in ref.…”

Section: Decision Treesmentioning

confidence: 99%

A review of data mining applications in crime

Hassani

Huang

Silva

et al. 2016

Statistical Analysis

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“…This website a breakthrough in the field of security technology on a location prepared to minimize the occurrence of repeated crime at that location [7]. This website is a pilot project that is proposed as a solution to the lack of information about the susceptibility of the crime scene in a city.…”

Section: Methodsmentioning

confidence: 99%

Design Website Portal Information Crime-Prone Location Using the Concept of Crowdsourcing

Maulana

Huda

2020

JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science)

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“…This approach is categorized as a supervision method that is able to label and identify the individual attributes of testing data based on the training data that has been provided to form a tree called decision tree. Decision tree can be used to design rules for deduction system and label the unlabeled data (Nasridinov et al, 2013). Decision tree is a well-known approach to classification the results of which are provided on a flow chart similar to a tree structure.…”

Section: Decision Treementioning

confidence: 99%

A novel hybrid support vector machine with decision tree for data classification

Gashti

2017

Int. j. adv. appl. sci.

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The purpose of this paper is to increase the accuracy of a proposed support vector machine model using hybrid model of SVM and ID3. Then the hybrid approach based on SVM and ID3 tree will be evaluated focusing on analyzing the impact of ID3 on SVM performance. The evaluation process was carried out on the global dataset and Adult reference extracted from KEEL dataset repository. The obtained results demonstrate higher classification accuracy (0.9125) of the proposed model compared to SVM and ID3.

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A Decision Tree-Based Classification Model for Crime Prediction

Cited by 29 publications

References 5 publications

A review of data mining applications in crime

A review of data mining applications in crime

Design Website Portal Information Crime-Prone Location Using the Concept of Crowdsourcing

A novel hybrid support vector machine with decision tree for data classification

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