Association Rule Mining Crash

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A first step in improving association rules algorithms is to break down the problem into two main steps. The first is to find all the sets of elements that meet the minimum support. This step is usually expensive because it requires several passes through the database (see Fig. 1). Data mining is defined as a non-trivial process of identifying valid, new, potentially useful, and ultimately understandable patterns in the data [1]. In fact, it is an essential part of business analysis and major trends in information technology. It includes many common classes of tasks (clustering, classification, association rules [2], etc.) designed for knowledge discovery in databases (KDD). The new contribution of this work is the application of these techniques to common business problems using computer-aided approaches with graphical interfaces, which means that the tools are easy to use and available to professionals. The technical architecture of the proposed approach is illustrated in Fig.

7. The implementation is based on R [32] and Shiny [33], the open source programming language and software environment for statistical calculations and graphs. The server consists of two components: the Rstudio server and the R packages for exploring and viewing association rules. Shiny is an R package that makes it easy to build interactive web applications directly with R. Individual components are customers; These are connected to a network and send a request to the server, and the server responds accordingly. The web application is interactive, scalable and suitable for traffic accident analysis. In summary, the integration of the association rule technique into multi-criteria decision analysis contributes to a better understanding of the dynamics of traffic accidents and can provide meaningful information that helps decision-makers and logistics managers to improve performance in terms of transport quality and road safety optimisation. Finally, the proposed approach has the following major advantages: data mining is often used as an approach that integrates concepts from statistics and artificial intelligence. Therefore, it is a powerful tool that can discover complex and hidden relationships in large data sets. It has a clear advantage over other traditional statistical methods, especially for complex systems; this is certainly the case in this study on the optimisation of road safety. In this section, we discuss the different steps used in the construction of the methodology we propose.

We start by developing the exploration of association rules as described below. The D`Apriori algorithm and its derivatives are an efficient solution for extracting association rules. However, these algorithms generate a large number of rules that prevent decision-makers from making their own selection of the most interesting rules. To solve this problem, integrating a multi-criteria business intelligence approach into practice for decision-makers affected by redundancy in the extracted rules is useful [29,30,31]. In this context, we use the ELECTRE TRI method, considering a set of extracted rules as alternatives and support, trust and buoyancy as criteria. Sze NN, Wong SC (2007) Diagnostic analysis of the logistics model for the severity of pedestrian injuries in road accidents. Accid Anal Prev 39:1267–1278 Ait-Mlouk A, Agouti T, Gharnati F, Derbali B (2015) A choice of relevant association rules based on a multi-criteria analysis approach. 2015 5th International Conference on Information and Communication Technologies and Accessibility (ICTA), Marrakech, pp. 1-6. doi:10.1109/ICTA.2015.7426886 In the field of data mining, Pγ association rules algorithms generate a large number of extracted rules that do not allow an expert to make his own selection of the most interesting. To solve this problem, the integration of MCDA and in particular the existing ELECTRE TRI method offers the possibility of sorting the results [26,27,28,29].

Oa J, Lpez G, Abelln J (2013) Extracting decision rules from police accident reports through decision trees. Accid Anal Prev 50:1151-1160 As a literature review shows, many data mining techniques have been proposed to analyze road accidents. In this context, Kuhnert et al. analyzed with CART and MARS an epidemiological case-control study on injuries caused by road accidents. They also identified potential risk areas that are largely caused by the situation of drivers [10]. Ossenbruggen et al. [3] used logistic regression models to analyze the factors involved in the accidents and found that commercial areas were more dangerous than village sites. Sohn et al. [11] used the three data mining techniques of decision trees, neural networks, and logistic regression to uncover important factors that affect the severity of Korean road traffic. Subsequently, Mio et al. [12] analyzed the severity of road accidents using a decision tree.

They found that the fatal injuries were caused by many factors, including seat belts, alcohol, and lighting conditions. Graphical visualization uses vertices and edges (see Fig. 10). Vertices typically represent elements or sets of elements, and edges indicate a relationship between rulers. Interesting dimensions are usually added to the path as labels for the edges. Lenca P, Meyer P, Vaillant B, Picouet P, Lallich S (2004) Evaluation and multi-criteria analysis of quality measures of association rules. Revue des Nouvelles Technologies de l`Information, mesures de Qualit pour la Fouille de Donnes, RNTI-E-1, pp. 219-246 Sanmiquel L, Rossell JM, Vintr C (2015) Study of Spanish mining accidents using data mining techniques. Saf Sci 75:49-55 In many countries, road accidents occur frequently, affecting transport and navigation services. Understanding road traffic is extremely important for improving road safety.

In this article, we propose an effective method to identify strong and relevant association rules from a database of road accidents. In order to identify the hidden links between the most common accidents, the traffic accident dataset is analysed using the association rules technique. The proposed method uses effective exploration of association rules. In addition, the integration of MCDA into the Association Rule Mining process provides a sustainable solution by selecting only the most interesting rules according to the preferences of decision-makers. In particular, we examine a set of rules extracted from the traffic accident database, taking into account the criteria most commonly used in the literature. We conclude that applying multi-criteria business intelligence to a set of extracted rules can help solve the problem that arises when using traditional algorithms in terms of redundancy and lack of interesting rules. In addition, the results suggest that human and behavioral characteristics play an important role in the development of all traffic accidents. Finally, the results show that the proposed approach fulfils its objective and can provide meaningful information that can help in the development of appropriate prevention strategies to improve road safety. This article explores a large-scale data mining technique known as Association Rule Mining, which can predict future accidents in advance and allow drivers to avoid dangers.

However, this technique creates a very large number of decision rules that prevent decision-makers from making their own selection of the most relevant rules. In this context, the integration of a multi-criteria decision analysis approach would be particularly useful for decision-makers concerned with the redundancy of extracted rules. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of elements in large databases. In: Proceedings of ACM SIGMOD Conference on Management of Data (SIGMOD), pp. 207-216 The association rule technique is a powerful method of data mining to determine the relationship between variables in large databases. It was proposed by Agrawal [2] for the analysis of transactional databases. It is defined as follows: Let I = {i 1 ,i 2 . i n } denotes the set of n binary elements, and leave D = {t 1 ,t 2 . t m } denotes the set of transactions. . .

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