This paper does not present finished work but discusses a current problem faced by the EMEA in detecting when a new safety problem with a marketed pharmaceutical product is emerging. This detection is based on analysis of a very large database of spontaneous reports by healthcare professionals. The mixture of complexity but also known structure within the data appear to call for a combination o...

A data mining algorithm builds a model that captures interesting aspects of the underlying data. We develop a framework for quantifying the difference, called the deviation, between two datasets in terms of the models they induce. In addition to being a quantitative, intuitively interpretable measure of difference, the deviation between two datasets can also be computed very fast. Our framework...

fuzzy rule-based classification system (frbcs) is a popular machine learning technique for classification purposes. one of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. however many cases the minority classes are more important than the majority ones. in this paper, we have extended ...

decision support systems (dsss) are prevalent information systems for decision making in many competitive business environments. in a dss, decision making process is intimately related to some factors which determine the quality of information systems and their related products. traditional approaches to data analysis usually cannot be implemented in sophisticated companies, where managers need...

decision support systems (dsss) are prevalent information systems for decision making in many competitive business environments. in a dss, decision making process is intimately related to some factors which determine the quality of information systems and their related products. traditional approaches to data analysis usually cannot be implemented in sophisticated companies, where managers nee...