Discovering Trends in Large Datasets Using Neural Networks

Kaikhah, Khosrow; Doddameti, Sandesh

doi:10.1007/s10489-006-6929-9

Cited by 20 publications

(6 citation statements)

References 9 publications

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“…Studying results indicate that the temporal patterns and port distributions can aid in the better understanding of crossing activity. Kaikhah and Doddameti (2006) proposed a tool to discover the existing trends for each type of crime (murder, rape, robbery, auto theft) in US cities, which used a new training process of neural network. The neural network is modified via pruning hidden layer activation clustering and is trained to learn the correlations and relationships that exist in a dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

An intelligent decision-support model using FSOM and rule extraction for crime prevention

Kuo

Tsai

2010

Expert Systems with Applications

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Section: Literature Reviewmentioning

confidence: 99%

An intelligent decision-support model using FSOM and rule extraction for crime prevention

Kuo

Tsai

2010

Expert Systems with Applications

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“…Ku et al (2012) applied visualization but he analyzed crime text report to facilitate analysis and decision making process. Kaikhah and Doddameti (2006) applied knowledge discovery technique but only focused for murder, rape, robbery and auto theft crime in US.…”

Section: Visualization Of Crime Datamentioning

confidence: 99%

“…Fuzzy clustering (Grubesic, 2006) No Burglary Classification One nearest neighbor (1NN) and a location constrained variation, Yes decision tree (J48), Support Vector Machine (SVM) with radial basis function, neural network, naive bayes (Yu et al, 2011) Murder, rape, Clustering Neural network (Kaikhah and Doddameti, 2006) No robbery and auto theft more than five percents. The manual process for decision making for crime prevention will produce inaccurate and inefficient result.…”

Section: Introductionmentioning

confidence: 99%

“…Grubesic (2006) used fuzzy clustering for hot-spot detection but he did not mention specific architecture. Kaikhah and Doddameti (2006) presented a novel knowledge discovery technique to discover trends using clustering technique for murder, rape, robbery and auto theft crime in United States cities. Furthermore, Yu et al (2011) applied classification technique for burglary crime.…”

Section: Introductionmentioning

confidence: 99%

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Architecture of Decision Support System for Crime Visualization

No¹,

Ab²,

Mohemad³

et al. 2015

J. of Applied Sciences

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A B S T R A C TRecently, crime rate in Malaysia is increasing from day to day. If this situation is not monitored and there is no drastic action taken, it may cause many serious problems. Therefore, crime prevention is one of the important components of an overall strategy to reduce crime and to strengthen public security. However, the main challenge faced by all law-enforcement and intelligence-gathering organizations is to analyze the growing volumes of crime data accurately and efficiently in order to make decision for crime prevention. Due to difficulty in decision making for crime prevention, Decision Support System (DSS) and data mining approach can be used to resolve the problem. Now-a-days, data mining approach has been exposed to be a practical decision-support concept in predicting and preventing crime. As a result, we propose architecture of DSS using visualization technique because it can represent crime data into more comprehensible presentation. The results from this proposed architecture can support the security authority in assessing more suitable law enforcement strategies, increasing the accuracy of selection decision making, as well as improving the use of security authority duty deployment for crime prevention.

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“…The police needs valuable information such as crime statistics in order to increase the accuracy of selection decisions and to avoid biased analysis. Over the last two decades, researchers have developed several techniques to support law enforcement activities in order to prevent criminal act (Grubesic, 2006;Noor et al, 2011) and discover patterns of crime (Kaikhah and Doddameti, 2006;Li et al, 2010;Phillips and Lee, 2012). These works come from disciplines of behavior and psychology, statistics and artificial intelligence (Li et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

A Review on a Classification Framework for Supporting Decision Making in Crime Prevention

No¹,

Hamid²,

Mohemad³

et al. 2014

J. of Artificial Intelligence

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Increasing volume of crimes has brought a serious problem to many countries across the world. Crime prevention is an important component of an overall strategy to reduce crime and to strengthen public safety. Although, Supporting Decision Making (SDM) in crime prevention is an important topic but a comprehensive literature review on the subject has yet to be implemented. Thus, this study presents a systematic and comprehensive review on a classification framework for SDM in crime prevention. Forty four journal articles on the subject published between 2000 and October, 2015 were analyzed and classified into two categories of index crime (violent crime and property crime) and six classes of data mining techniques (prediction, classification, visualization, regression, clustering and outlier detection). The results of this study clearly show that data mining especially prediction and clustering techniques have been applied most extensively in both index crime categories. The main data mining techniques used for SDM in crime prevention are Bayesian, neural network and nearest neighbor. This study also addresses the gaps between SDM in crime prevention and the needs of practitioners to encourage more researches in crime analysis. Finally, it concludes with some suggestions for future research on SDM in crime prevention.

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Discovering Trends in Large Datasets Using Neural Networks

Cited by 20 publications

References 9 publications

An intelligent decision-support model using FSOM and rule extraction for crime prevention

An intelligent decision-support model using FSOM and rule extraction for crime prevention

Architecture of Decision Support System for Crime Visualization

A Review on a Classification Framework for Supporting Decision Making in Crime Prevention

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