Intelligent criminal identification system

Bogahawatte, Kaumalee; Adikari, Shalinda

doi:10.1109/iccse.2013.6553986

Cited by 18 publications

(9 citation statements)

References 3 publications

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“…Bogahawatte and Adikari [2] proposed an approach in which they highlighted the usage of data mining techniques, clustering and classification for effective investigation of crimes and criminal identification by developing a system named Intelligent Crime Investigation System (ICSIS) that could identify a criminal based up on the evidence collected from the crime location. They used clustering to identify the crime patterns which are used to commit crimes knowing the fact that each crime has certain patterns.…”

Section: Crime Patterns and Evidence-based Methodsmentioning

confidence: 99%

“…When considering crime patterns and evidence based methods, there are clustering and classification based papers. Bogahawatte and Adikari [2] concentrated on using the Naïve Bayes for finding out most possible suspect. Jyoti Agarwal et al [3] on the other hand focused on crime analysis by implementing the K-Means clustering algorithm on crime dataset using rapid miner tool and the author had performed the crime analysis by considering the homicide crimes and plotting it with respect to year.…”

Section: Qualitative Analysis Of Crime Analysis and Prediction Approamentioning

confidence: 99%

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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%

Section: Qualitative Analysis Of Crime Analysis and Prediction Approamentioning

confidence: 99%

Survey on Crime Analysis and Prediction Using Data Mining Techniques

David

Suruliandi

2017

IJSC

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“…The main advantage of this field work is to create criminal database with real attributes. Database is created in Attribute Relation File Format (ARFF) [7] [8]. Once attributes and their data type are defined, it is very easy to add records.…”

Section: Techniquesmentioning

confidence: 99%

History Bits based novel algorithm for classification of structured data

Joshi

Sakhare²

2015

2015 IEEE International Advance Computing Conference (IACC)

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Data mining is very effective technique for extracting useful information from large amount of structured dataset. Number of algorithms are available that can mine the useful and relevant information. Use of particular data mining algorithm has great impact on the results obtained. An innovative classification algorithm based on History Bits is developed for extracting useful and relevant information from large structured dataset. For implementation and testing of the History Bits based algorithm we have designed a structured criminal dataset. This algorithm analyses criminal information in lesser time and reduces the constraints of manual investigation process.

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“…. System design and process of PrepSearch [12] In [13] researches have introduced intelligent criminal identification system called ICIS which can potentially distinguish a criminal in accordance with the observations collected from the crime location for a certain class of crimes. The system uses existing evidences in situations for identifying a criminal by clustering mechanism to segment crime data in to subsets, and the Nave Bayesian classification has used for identifying possible suspect of crime incidents.…”

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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Intelligent criminal identification system

Cited by 18 publications

References 3 publications

Survey on Crime Analysis and Prediction Using Data Mining Techniques

Survey on Crime Analysis and Prediction Using Data Mining Techniques

History Bits based novel algorithm for classification of structured data

Crime Detection Technique Using Data Mining and K-Means

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