Fuzzy association rule mining for community crime pattern discovery

Buczak, Anna L.; Gifford, Chris

doi:10.1145/1938606.1938608

Cited by 74 publications

(31 citation statements)

References 13 publications

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“…Unfortunately, simplicity of definitions for crisp rules sometimes led to oversimplified definitions for fuzzy rules. Some authors believe the generalization of 2 Advances in Fuzzy Systems those crisp measures for fuzzy data is as trivial as substituting crisp terms with analogous fuzzy terminology inside of crispcase definitions; see, for example, [17,18]. Unfortunately, as discussed in this paper, such oversimplification may lead to erroneous outputs.…”

Section: Introductionmentioning

confidence: 89%

“…A naive approach for introducing lift, leverage, and conviction into the fuzzy association rules framework is to use simply their definitions (7), (8), and (9) for binary rules and replace binary support (4) and (5) and confidence (6) with their fuzzy alternatives (12) as, for example, in [17,18]. Unfortunately, that approach works well only for ⊗ being the product -norm.…”

Section: Naive Definition Of Lift Leverage and Conviction For Fuzzymentioning

confidence: 99%

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Interest Measures for Fuzzy Association Rules Based on Expectations of Independence

Burda

2014

Advances in Fuzzy Systems

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Lift, leverage, and conviction are three of the best commonly known interest measures for crisp association rules. All of them are based on a comparison of observed support and the support that is expected if the antecedent and consequent part of the rule were stochastically independent. The aim of this paper is to provide a correct definition of lift, leverage, and conviction measures for fuzzy association rules and to study some of their interesting mathematical properties.

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Section: Introductionmentioning

confidence: 89%

Section: Naive Definition Of Lift Leverage and Conviction For Fuzzymentioning

confidence: 99%

Interest Measures for Fuzzy Association Rules Based on Expectations of Independence

Burda

2014

Advances in Fuzzy Systems

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“…The analyses they conducted on a dataset containing crime information from 1991 to 1999 for the city of Philadelphia, US, indicated the existence of multi-scale complex relationships both in space and time. Using demographic information statistics at community (town) level, Buczak and Gifford [9] applied fuzzy association rule mining in order to derive a finite (and consistent among US states) set of rules to be applied by crime analysts. Other common models are the ones proposed by Eck et al [16] and by Chainey et al [11] that rely on kernel density estimation from the criminal history record of a geographical area.…”

Section: Related Workmentioning

confidence: 99%

Once Upon a Crime

Bogomolov

Lepri

Staiano

et al. 2014

Proceedings of the 16th International Conference on Multimodal Interaction

208

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In this paper, we present a novel approach to predict crime in a geographic space from multiple data sources, in particular mobile phone and demographic data. The main contribution of the proposed approach lies in using aggregated and anonymized human behavioral data derived from mobile network activity to tackle the crime prediction problem. While previous research efforts have used either background historical knowledge or offenders' profiling, our findings support the hypothesis that aggregated human behavioral data captured from the mobile network infrastructure, in combination with basic demographic information, can be used to predict crime. In our experimental results with real crime data from London we obtain an accuracy of almost 70% when predicting whether a specific area in the city will be a crime hotspot or not. Moreover, we provide a discussion of the implications of our findings for data-driven crime analysis.

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“…Many classic data mining techniques have been successful for crime analysis generally, such as association rule mining [7][8][9][10], classification [11], and clustering [6]. We refer to the general overview of Chen et al [12], in which the authors present a general framework for crime data mining, where many of these standard tools are available as part of the COPLINK [13] software package.…”

Section: Background and Related Workmentioning

confidence: 99%

Learning to Detect Patterns of Crime

Wang

Rudin

Wagner³

et al. 2013

Advanced Information Systems Engineering

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Abstract. Our goal is to automatically detect patterns of crime. Among a large set of crimes that happen every year in a major city, it is challenging, time-consuming, and labor-intensive for crime analysts to determine which ones may have been committed by the same individual(s). If automated, data-driven tools for crime pattern detection are made available to assist analysts, these tools could help police to better understand patterns of crime, leading to more precise attribution of past crimes, and the apprehension of suspects. To do this, we propose a pattern detection algorithm called Series Finder, that grows a pattern of discovered crimes from within a database, starting from a "seed" of a few crimes. Series Finder incorporates both the common characteristics of all patterns and the unique aspects of each specific pattern, and has had promising results on a decade's worth of crime pattern data collected by the Crime Analysis Unit of the Cambridge Police Department.

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Fuzzy association rule mining for community crime pattern discovery

Cited by 74 publications

References 13 publications

Interest Measures for Fuzzy Association Rules Based on Expectations of Independence

Interest Measures for Fuzzy Association Rules Based on Expectations of Independence

Once Upon a Crime

Learning to Detect Patterns of Crime

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