Although the integration of engineering data within the framework of product data management systems has been successful in the recent years, the holistic analysis (from a systems engineering perspective) of multi-disciplinary data or data based on different representations and tools is still not realized in practice. At the same time, the application of advanced data mining techniques to compl...

traditional leveraging statistical methods for analyzing today’s large volumes of spatial data have high computational burdens. to eliminate the deficiency, relatively modern data mining techniques have been recently applied in different spatial analysis tasks with the purpose of autonomous knowledge extraction from high-volume spatial data. fortunately, geospatial data is considered a proper s...

In this paper, we introduce a hybrid approach for analyzing metabolomic data about the so-called diabetes of type 2 . The identi cation of biomarkers which are witness of the disease is very important and can be guided by data mining methods. The data to be analyzed are massive and complex and are organized around a small set of individuals and a large set of variables (attributes). In this stu...

Mounting amounts of data made traditional data analysis methods impractical. Data mining (DM) tools provide a useful for alternative framework that addresses this problem. This study follows a DM technique to identify diabetic patients. We develop a model that clusters diabetes patients of a large healthcare company into different subpopulation. Consequently, we show the value of applying a DM ...

ÐThe exploration of heterogenous information spaces requires suitable mining methods as well as effective visual interfaces. Most of the existing systems concentrate either on mining algorithms or on visualization techniques. This paper describes a flexible framework for Visual Data Mining which combines analytical and visual methods to achieve a better understanding of the information space. W...

Our research explores the use of ensemble, or multistrategy learning techniques for inducing and managing patterns of knowledge from organizational data. Specifically, we are exploring the use of data mining techniques in building an ensemble classification system – i.e., a system that incorporates multiple machine learning techniques to generate multiple models from existing data and make pred...

Weconsider a network representation of the stockmarket data referred to as themarket graph,which is constructed by calculating cross-correlations between pairs of stocks based on the opening prices data over a certain period of time. We study the evolution of the structural properties of the market graph over time and draw conclusions regarding the dynamics of the stock market development based...