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- PublicationMétadonnées seulementExtraction intelligente de connaissances axée sur les processus dans le cadre du renseignement criminel
- PublicationMétadonnées seulementAn Intelligent Process-driven Knowledge Extraction Framework for Crime AnalysisIn this research, we attempt to study the contribution of data mining techniques in crime analysis and intelligence. It is an interdisciplinary project, combining forensic, criminological and computational methods. We search to develop a frame in which data mining techniques, driven by crime analysis and forensic processes, take an active part to data interpretation and information analysis (in order to extract knowledge). Realized in collaboration with the cantonal police forces of Vaud in Switzerland, the first phase of this project consists to focus on residential burglary data. The sample is provided by the Concept Intercantonal de Coordination Opérationnelle et Préventive (CICOP) database, which is the regional center for crime analysis in French-speaking part of Switzerland. The CICOP analysts use phenomenon codes to define a particular crime situation. These CICOP codes are directly related to the situational approaches in criminology. Concretely, we have three main purposes: residential burglary classification, new phenomena discovery, and series or trends detection. That brings, in first hand, to formalize processes identified in crime analysis with the help of a standard notation called Business Process Modeling Notation (BPMN). Then, different data mining techniques are tested on data, and assessed by confronting them with phenomena identified by police forces analysts. Finally, we make a criminological analysis on the results to check the consistency with main situational theories in criminology. Accuracy and results relevance exam is an important step, because the different data mining algorithms can generate trivial and unexplainable rules. We note then the need of a human interpretation, and in this case, of a criminological interpretation. The first results are hopeful and classification algorithms are effective to classify residential burglaries like CICOP analysts did it.
- PublicationMétadonnées seulement
- PublicationMétadonnées seulementChange points detection in crime-related time series: An on-line fuzzy approach based on a shape space representationThe extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.