Crime Analysis using K-Means Clustering

Agarwal, Jyoti; Nagpal, Renuka; Sehgal, Rajni

doi:10.5120/14433-2579

Cited by 77 publications

(61 citation statements)

References 5 publications

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“…Agarwal et al [3] used the rapid miner tool for analyzing the crime rates and anticipation of crime rate using different data Crime Prediction Crime clusters mining techniques. Their work done is for crime analysis using the K-Means Clustering algorithm.…”

Section: Crime Patterns and Evidence-based Methodsmentioning

confidence: 99%

Survey on Crime Analysis and Prediction Using Data Mining Techniques

David

Suruliandi

2017

IJSC

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Data Mining is the procedure which includes evaluating and examining large pre-existing databases in order to generate new information which may be essential to the organization. The extraction of new information is predicted using the existing datasets. Many approaches for analysis and prediction in data mining had been performed. But, many few efforts has made in the criminology field. Many few have taken efforts for comparing the information all these approaches produce. The police stations and other similar criminal justice agencies hold many large databases of information which can be used to predict or analyze the criminal movements and criminal activity involvement in the society. The criminals can also be predicted based on the crime data. The main aim of this work is to perform a survey on the supervised learning and unsupervised learning techniques that has been applied towards criminal identification. This paper presents the survey on the Crime analysis and crime prediction using several Data Mining techniques.

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Section: Crime Patterns and Evidence-based Methodsmentioning

confidence: 99%

Survey on Crime Analysis and Prediction Using Data Mining Techniques

David

Suruliandi

2017

IJSC

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“…al. [1] introduced a framework for crime trends using a new distance measure for comparing all individuals based on their profiles and then clustering them accordingly. Manish Gupta et.…”

Section: Clustering By K-means Algorithmmentioning

confidence: 99%

Crime Detection Technique Using Data Mining and K-Means

Wadhai¹,

Kakade²,

Bokde³

et al. 2018

IJERT

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Abstract-Crimes will somehow influence organizations and institutions when occurred frequently in a society. Thus, it seems necessary to study reasons, factors and relations between occurrence of different crimes and finding the most appropriate ways to control and avoid more crimes. The main objective of this paper is to classify clustered crimes based on occurrence frequency during different years. Data mining is used extensively in terms of analysis, investigation and discovery of patterns for occurrence of different crimes. We applied a theoretical model based on data mining techniques such as clustering and classification to real crime dataset recorded by police in England and Wales within 1990 to 2011. We assigned weights to the features in order to improve the quality of the model and remove low value of them. The Genetic Algorithm (GA) is used for optimizing of Outlier Detection operator parameters using RapidMiner tool.

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“…Prediction of crime primarily based on spatial distribution of existing data and anticipation of crime charge using distinct data mining techniques 3. Detection of crime [5].…”

Section: Crime Detectionmentioning

confidence: 99%

Hybrid Technique Based on Clustering for Crime Detection in Data Mining

2017

IJAERD

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Crime Analysis using K-Means Clustering

Cited by 77 publications

References 5 publications

Survey on Crime Analysis and Prediction Using Data Mining Techniques

Survey on Crime Analysis and Prediction Using Data Mining Techniques

Crime Detection Technique Using Data Mining and K-Means

Hybrid Technique Based on Clustering for Crime Detection in Data Mining

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