Conference Paper

An Association Model Based on Modus Operandi Mining for Implicit Crime Link Construction

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Link analysis has been an important tool in crime investigation. Explicit or implicit social links, such as kinship, financial exchange, telephone connection, links derived from modus operandi, time of day, and geographic relationship, are often used to construct links between criminals. This paper proposes an association model based on modus operandi mining to establish links among crime cases and chronic criminals. Two data sets of robbery and residential burglary crime records were collected from a local police department and were used for experiment to evaluate the performance of the proposed approach. Explicit social links (12), such as friendship, kinship, financial exchange, sexual relationships, and telephone connections, are often used to construct social network among suspects and victims in crime investigations. Although implicit crime links, such as links derived from modus operandi, geographic relationship, and crime time of day, are valuable and oftenly used in crime profiling, the effectiveness of their usage is mainly depending on the interpretation and expertise of an investigator. While social link analysis of solved crime cases could be based solely on explicit links, associations among unsolved ones can only be constructed by implicit links derived from mining crime databases. According to Locard's Exchange Principle: every contact of the perpetrators of a crime scene leaves a trace;the perpetrators will both bring something into the scene and leave with something from the scene(13). Furthermore, the type、quantity、position、and status of evidences left at a crime scene often provides clues for crime reconstruction (5). By analyzing evidences left at a crime scene, a criminal profiling specialist can derive the information needed to reconstruct a crime and narrow down the suspects (10). Criminal profiling is mainly based on 'method of operations' or modus operandi, such as preparation actions, crime methods and weapon(s), etc. It has been shown that after a criminal gets used to a certain method of operation, he/she will use the same modus operandi again in committing other cases (6). Therefore, modus operandi information not only can be used to identify the relationship between suspects and crime cases, but also to discover the association among unsolved cases (11). In Taiwan, the final step of crime investigation procedure is to file a report for each case. Besides essential case data, such as case category, location, time of a day, suspect's and victim's names, a crime record also includes five modus operandi variables, namely crime cause(CC), crime habit(CH), preparation action(PA), crime method(CM), and crime tool(CT). Each modus operandi variable has a set of predefined values for selection in report filing. This paper proposes an association model based on modus operandi mining to construct links among crime cases and chronic criminals. Two local police data sets of 1504 robbery cases and 5443 residential burglary cases were collected and used for experiment to evaluate the proposed approach. The organization of this paper is as follows: section 2 gives related literatures, section 3 describes the proposed model, section 4 presents the experimental results, and section 5 gives conclusions of this paper.

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... In addition to the above, researchers also have utilized behavioral features of the crime pattern with machine learning algorithms including logistic regression (Bennell & Canter, 2002;Tonkin, Woodhams, Bull, & Bond, 2012), probability inference (Wang & Lin, 2011), etc. for eliciting associations between crime and criminals. Bache, Crestani, Canter, and Youngs (2010) applied unigram language model i.e. multinomial and multiple Bernoulli models over solved crimes dataset to link behavioral features with characteristics of offenders and found that Bernoulli models outperformed multinomial models. ...
... It is accomplished through a multi-level association model (shown in Fig. 2). This model is based on spatial-temporal characteristics (Ozgul, Gok, Erdem, & Ozal, 2012) and modus operandi behavior N. Qazi and B.L.W. Wong Information Processing and Management 56 (2019) 102066 (Wang & Lin, 2011) of the given crime pattern. We compared the similarity of the given crime pattern with the other crime entities to establish these multi-level associations. ...
... We here cite some of the related projects that involve association mining tasks.Among such projects is the Prep-Search [5] that answers only WHO and WHERE questions identifying suspects and their locations.The CrimeLink Explorer [6] identifies associations between people only, without revealing any associations of other entity types such as location or vehicle. They employed shortest path algorithm, co-occurrence analysis, and a heuristic approach over the structured crime incident data extracted from the Tucson Police Department(TPD) Records Management System.Some researchers [7] however have employed modus operandi based similarity component of the crime to establish associations among the crime cases and chronic criminals on burglary and robbery dataset. Introducing notion of prominent criminal communities, [8] have proposed a social network mining method to extract groups from unstructured textual data achieved from a suspects hard drive. ...
... He has given three principles of associations which are resemblance contiguity in time and place and causality. References [7] also has emphasized the use of modus operandi to determine the association with a chronic offender. It has also been shown that after a criminal gets used to a certain method of operation, he/she will use the same modus operandi again in committing other cases. ...
... As redes sociais são usadas para diversas finalidades desde a análise da influência social, um fenômeno em que as ações de um usuário podem induzir seus amigos a se comportar de maneira similar , associação entre criminosos e crimes [Wang and Lin 2011], chegando até mesmo a medição de semelhanças entre grupos de terroristas [Ozgul et al. 2011] e sua evolução [Nizamani and Memon 2011]. Outro importante foco das redes sociaisé a análise da colaboração entre pesquisadores, através de redes sociais de co-autoria [Reijers et al. 2009] [Newman 2004b] [Newman 2004a]. ...
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Crimes have been existing since the dawn of human evolution. Its existence has been a threat to the society. The ever-changing nature of crimes in the world is creating challenges to the police department as well as the investigating agencies of various wings. To keep up with the dynamic nature of crimes and the complexity of the crimes committed are being surfaced every day, and solving them became an entanglement to an investigative officer. Due to the lack of perception or human error in judging an object in the crime scene as a potential evidence, many crimes over the decades took so many years to solve and some remained a mystery. The paper presents state of the art for ontologies-based approaches that will try to help certain investigative officer in not only piling up the evidences collected but also draw out a pattern that might suggest him the approach to solve the case in no time. This system focusses mainly on organized crimes due to most of the unsolved crimes that were being a part of it.
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This is the third edition of the premier professional reference on the subject of data mining, expanding and updating the previous market leading edition. This was the first (and is still the best and most popular) of its kind. Combines sound theory with truly practical applications to prepare students for real-world challenges in data mining. Like the first and second editions, Data Mining: Concepts and Techniques, 3rd Edition equips professionals with a sound understanding of data mining principles and teaches proven methods for knowledge discovery in large corporate databases. The first and second editions also established itself as the market leader for courses in data mining, data analytics, and knowledge discovery. Revisions incorporate input from instructors, changes in the field, and new and important topics such as data warehouse and data cube technology, mining stream data, mining social networks, and mining spatial, multimedia and other complex data. This book begins with a conceptual introduction followed by a comprehensive and state-of-the-art coverage of concepts and techniques. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. Wherever possible, the authors raise and answer questions of utility, feasibility, optimization, and scalability. relational data. -- A comprehensive, practical look at the concepts and techniques you need to get the most out of real business data. -- Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning, -- Scores of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects. -- Complete classroom support for instructors as well as bonus content available at the companion website. A comprehensive and practical look at the concepts and techniques you need in the area of data mining and knowledge discovery.
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