![]() To make RFs accessible to experts with no data analytics background, we present RfX, a Visual Analytics (VA) system for the analysis of a RF's decision-making process. The interpretation of a given RF usually relies on the analysis of statistical values and is often only possible for data analytics experts. Random Forests (RFs) are a machine learning (ML) technique widely used across industries. The research methodology as well as the gathered In this perspective, this paper wants to share the experience conducted in using a public dataset containing information related to criminals in order to both (i) extract specific features and to build a model for the detection of terrorists on Facebook social network, and (ii) to highlight the current limits. However, since the OSNs domain is relatively young, a variety of issues related to data availability makes it difficult to apply and immediately benefit from such techniques, in supporting the detection of criminals on OSNs. ![]() Machine learning techniques have shown benefits in problem solving belonging to different application domains, when, due to the huge dimension in terms of data and variables to consider, it is not feasible their manual assessment. Unfortunately, due to their vast usage, it is not always trivial to identify criminals using them unlawfully. As they are freely accessible and easy to use, criminals abuse of them for achieving their purposes, for example, by spreading propaganda and radicalising people. Online social networks (OSNs) represent powerful digital tools to communicate and quickly disseminate information in a unofficial way. Our comparisons indicate that our proposed approach performs better than existing approaches, because it takes into account the values of the branches in the trees through sequential pattern mining. Our experimental studies compared the results of these novel similarity measures and also compared our approach with existing approaches. After the construction of decision trees from different data marts using a classification algorithm, sequential pattern mining was applied to the decision trees to obtain rules, and then the k-nearest neighbor algorithm was performed on these rules to compute similarities using two novel measures: General similarity and pieced similarity. This study proposes DTreeSim, a new approach that applies multiple data mining techniques (classification, sequential pattern mining, and k-nearest neighbors) sequentially to identify similarities among decision trees. The main objective of this study is to compute the similarity of decision trees using data mining techniques. There have been multiple perspectives and multiple calculation techniques to measure the similarity of two decision trees, such as using a simple formula or an entropy measure. A number of recent studies have used a decision tree approach as a data mining technique some of them needed to evaluate the similarity of decision trees to compare the knowledge reflected in different trees or datasets. ![]()
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