An interactive human centered data science approach towards crime pattern analysis

Qazi, Nadeem; Wong, B. L. William

doi:10.1016/j.ipm.2019.102066

Cited by 24 publications

(13 citation statements)

References 69 publications

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“…The distance computes the root of square differences between a pair of documents. It is calculated by (4).…”

Section: Text Clusteringmentioning

confidence: 99%

“…In general, it is a first-hand information report containing narratives about an incident in which a crime or offence is suspected [3]. The hidden but useful information that lies within police reports necessitates a high level of skill on the part of criminal investigators to manually and regularly analyze the reports to find crime patterns and trend correlations, which is a challenging task due to the large volume of reports in an unstructured format [4]. The issues arise when too many reports are analyzed at the same time, and not enough criminal investigators are  ISSN: 2088-8708 Int J Elec & Comp Eng, Vol.…”

Section: Introductionmentioning

confidence: 99%

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Performance analysis in text clustering using k-means and k-medoids algorithms for Malay crime documents

Mohemad

Muhait²,

Noor³

et al. 2022

IJECE

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<span lang="EN-US">Few studies on text clustering for the Malay language have been conducted due to some limitations that need to be addressed. The purpose of this article is to compare the two clustering algorithms of k-means and k-medoids using Euclidean distance similarity to determine which method is the best for clustering documents. Both algorithms are applied to 1000 documents pertaining to housebreaking crimes involving a variety of different modus operandi. Comparability results indicate that the k-means algorithm performed the best at clustering the relevant documents, with a 78% accuracy rate. K-means clustering also achieves the best performance for cluster evaluation when comparing the average within-cluster distance to the k-medoids algorithm. However, k-medoids perform exceptionally well on the Davis Bouldin index (DBI). Furthermore, the accuracy of k-means is dependent on the number of initial clusters, where the appropriate cluster number can be determined using the elbow method.</span>

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“…The distance computes the root of square differences between a pair of documents. It is calculated by (4).…”

Section: Text Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance analysis in text clustering using k-means and k-medoids algorithms for Malay crime documents

Mohemad

Muhait²,

Noor³

et al. 2022

IJECE

View full text Add to dashboard Cite

show abstract

“…Beyond this, it has become evident that traditional approaches to archival formation and management are not suited to the vast quantities of data that archives are now required to process, but that they may in fact prevent the users from obtaining the data that they are seeking ( [85], [86]). This situation is ultimately undermining one of the key functions of the archive, and is destined to become a more complex issue as the already vast quantities of digital material increase requiring additional metadata, improved interfaces and a more digitally focused skillset for the archivists and the users ( [104][83][93]).…”

Section: The Archive and The End-usermentioning

confidence: 99%

Leveraging digital forensics and data exploration to understand the creative work of a filmmaker: A case study of Stephen Dwoskin's digital archive

Bartliff

Kim

Hopfgartner

et al. 2020

Information Processing & Management

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This paper aims to establish digital forensics and data exploration as a methodology for supporting archival practice and research into a filmmaker's creative processes. We approach this by exploring the digital legacy hard drives of the late artist Stephen Dwoskin , who is recognised as an influential filmmaker at the forefront of the shift from analogue to digital film production. The research findings of this case study show that digital forensics is effective in extracting a timeline of hard drive activities, data that can be explored to reveal clues about the artist's personal/professional history, stages of creative processes, and technical environment.The paper further demonstrates how this is related to current thinking around user-centred archival workflow and understanding of creative processes. The broader impact of the work for advancing digital archiving and research into creative processes is highlighted, concluding with a discussion of how, going forward, the approach can be coupled with deeper content analysis to reveal what influences editing choices taking place over time.

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“…Furthermore, data-mining methods such as entity extraction, clustering/classification technique, and social network analysis make it possible to efficiently explore large data. Network visualization enables an investigator to intuitively recognize the crime pattern [32][33][34].…”

Section: Traditional Criminal Profiling and Cybercrime Profilingmentioning

confidence: 99%

CBR-Based Decision Support Methodology for Cybercrime Investigation: Focused on the Data-Driven Website Defacement Analysis

Han

Kwak

Kim

2019

Security and Communication Networks

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Criminal profiling is a useful technique to identify the most plausible suspects based on the evidence discovered at the crime scene. Similar to offline criminal profiling, in-depth profiling for cybercrime investigation is useful in analysing cyberattacks and for speculating on the identities of the criminals. Every cybercrime committed by the same hacker or hacking group has unique traits such as attack purpose, attack methods, and target. These unique traits are revealed in the evidence of cybercrime; in some cases, these unique traits are well hidden in the evidence such that it cannot be easily perceived. Therefore, a complete analysis of several factors concerning cybercrime can provide an investigator with concrete evidence to attribute the attacks and narrow down the scope of the criminal data and grasp the criminals in the end. We herein propose a decision support methodology based on the case-based reasoning (CBR) for cybercrime investigation. This study focuses on the massive data-driven analysis of website defacement. Our primary aim in this study is to demonstrate the practicality of the proposed methodology as a proof of concept. The assessment of website defacement was performed through the similarity measure and the clustering processing in the reasoning engine based on the CBR. Our results show that the proposed methodology that focuses on the investigation enables a better understanding and interpretation of website defacement and assists in inferring the hacker’s behavioural traits from the available evidence concerning website defacement. The results of the case studies demonstrate that our proposed methodology is beneficial for understanding the behaviour and motivation of the hacker and that our proposed data-driven analytic methodology can be utilized as a decision support system for cybercrime investigation.

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An interactive human centered data science approach towards crime pattern analysis

Cited by 24 publications

References 69 publications

Performance analysis in text clustering using k-means and k-medoids algorithms for Malay crime documents

Performance analysis in text clustering using k-means and k-medoids algorithms for Malay crime documents

Leveraging digital forensics and data exploration to understand the creative work of a filmmaker: A case study of Stephen Dwoskin's digital archive

CBR-Based Decision Support Methodology for Cybercrime Investigation: Focused on the Data-Driven Website Defacement Analysis

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