Silvia Salini

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The complexity and variety of bibliographic data is growing, and efforts to define new methodologies and techniques for bibliometric analysis are intensifying. In this complex scenario, one of the most crucial issues is the quality of data and the capability of bibliometric analysis to cope with multiple data dimensions. Although the problem of enforcing a(More)
Association rules are one of the most popular unsupervised data mining methods. Once obtained, the list of association rules extractable from a given dataset is compared in order to evaluate their importance level. The measures commonly used to assess the strength of an association rule are the indexes of support, confidence, and lift. Relative Linkage(More)
When a research infrastructure is funded and implemented, new information and new publications are created. This new information is the measurable output of discovery process. In this paper, we describe the impact of infrastructure for physics experiments in terms of publications and citations. In particular, we consider the Large Hadron Collider (LHC)(More)
Association rules are one of the most popular unsupervised data mining methods. Once obtained, the list of association rules ex-tractable from a given dataset is compared in order to evaluate their importance level. The measures commonly used to assess the strength of an association rule are the indexes of support, confidence, and the lift. Relative Linkage(More)
The focus of the paper is the use of optimal scaling techniques to reduce the dimensionality of ordinal variables describing the quality of services to a continuous score interpretable as a measure of operational risk. This new score of operational risk is merged with a financial risk score in order to obtain an integrated measure of risk. The proposed(More)
In this paper two different non-classic methods, based on the analysis of qualitative data, are applied to evaluate customer satisfaction. The airline industry is considered with British Airways used as a case study. First, a classification algorithm based on the decision tree theory is performed. By conserving the original ordinal measuring scale for the(More)
Topic models are a well known clustering approach for textual data, which provides promising applications in the bibliometric context for the purpose of discovering scientific topics and trends in a corpus of scientific publications. However, topic models per se provide poorly descriptive metadata featuring the discovered clusters of publications and they(More)
This paper applies a novel technique of opinion analysis over social media data with the aim of proposing a new indicator of perceived and subjective well-being. This new index, namely SWBI, examines several dimension of individual and social life. The indicator has been compared to some other existing indexes of well-being and health conditions in Italy:(More)