1,720,969 research outputs found
An improved approach for association rule mining using a multi-criteria decision support system: a case study in road safety
No full textAbstract Purpose Road accidents have come to be considered a major public health problem worldwide. The aim of many studies is therefore to identify the main factors contributing to the severity of crashes. Methods This paper examines a large-scale data mining technique known as association rule mining, which can predict future accidents in advance and allow drivers to avoid the dangers. However, this technique produces a very large number of decision rules, preventing 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 affected by the redundancy of the extracted rules. Conclusion An analysis of road accidents in the province of Marrakech (Morocco) between 2004 and 2014 shows that the proposed approach serves this purpose; it may provide meaningful information that could help in developing suitable prevention policies to improve road safety
Multi-agent-based modeling for extracting relevant association rules using a multi-criteria analysis approach
No full textAbstract Recently, association rule mining plays a vital role in knowledge discovery in database. In fact, in most cases, the real datasets lead to a very large number of rules, which do not allow users to make their own selection of the most relevant. The difficult task is mining useful and non-redundant rules. Several approaches have been proposed, such as rule clustering, informative cover method and quality measurements. Another way to selecting relevant association rules, we believe that it is necessary to integrate a decisional approach within the knowledge discovery process. Therefore, in this paper, we propose an approach to discover a category of relevant association rules based on multi-criteria analysis. In other side, the general process of association rules extraction becomes more and more complex, to solve such problem, we also proposed a multi-agent system for modeling the different process of our proposed approach. Therefore, we conclude our work by an empirical study applied to a set of banking data to illustrate the performance of our approach
Mining and prioritization of association rules for big data: multi-criteria decision analysis approach
No full textAbstract Data mining techniques and extracting patterns from large datasets play a vital role in knowledge discovery. Most of the decision makers encounter a large number of decision rules resulted from association rules mining. Moreover, the volume of datasets brings a new challenge to extract patterns such as the cost of computing and inefficiency to achieve the relevant rules. To overcome these challenges, this paper aims to build a learning model based on FP-growth and Apache Spark framework to process and to extract relevant association rules. We also integrate the multi-criteria decision analysis to prioritize the extracted rules by taking into account the decision makers subjective judgment. We believe that this approach would be a useful model to follow, particularly for decision makers who are suffering from conflicts between extracted rules, and difficulties of building only the most interesting rules. Experimental results on road accidents analysis show that the proposed approach can be efficiently achieved more association rules with a higher accuracy rate and improve the response time of the proposed algorithm. The results make clear that the proposed approach performs well and can provide useful information that could help the decision makers to improve road safety
An improved approach for association rule mining using a multi-criteria decision support system: a case study in road safety
No full text
A Web-Based Platform for Mining and Ranking Association Rules
No full textIn this demo, we introduce an interactive system, which effectively applies multiple criteria analysis to rank association rules. We first use association rules techniques to explore the correlations between variables in given data (i.e., database and linked data (LD)), and secondly apply multiple criteria analysis (MCA) to select the most relevant rules according to user preferences. The developed system is flexible and allows intuitive creation and execution of different algorithms for an extensive range of advanced data analysis topics. Furthermore, we demonstrate a case study of association rule mining and ranking on road accident data.</p
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