health databases contain a wide scope of clinical data to explore relationships and patterns that can lead to new medical knowledge.today, the emergence of integrated information systems and growth of information technologies have better highlighted the importance of such databases. data mining is among the technological advances toward data management whose integration with traditional methods...

DATA mining (DM) consists of extracting interesting knowledge from real-world, large and complex data sets; and is the core step of a broader process, called knowledge discovery from databases (KDD). In addition to the DM step, which actually extracts knowledge from data, KDD process includes several preprocessing (data preparation) and postprocessing (knowledge refinement) steps. The goal of d...

The main aim of this project is the development of an intelligent system for Data Mining (DM) with the following features: It must be able to response to the different environments that any user may propose. Specially, it have to deal with imprecise and uncertain data and requirements. It must be able to provide information about the ”quality” of the obtained knowledge, by giving alternative te...

We present a new approach to content determination and content organization in the context of natural language generation for quantitative database summaries. Three key properties make our work innovative and interesting: (1) we developed a new text planning approach to deals with the content organization of a data set into a summary report, for example a Data Mining discovery; (2) the approach...

The increasing availability of automated data collection tools, database technologies and Information and Communication Technologies in biomedicine and health care have led to huge amounts of biomedical and healthcare data accumulated in several repositories. Unfortunately, the process of analysis of such data represents a complex task also because data volumes grow exponentially so manual anal...

Discovery of frequent patterns has been studied in a variety of data mining (DM) settings. In its simplest form, known from association rule mining, the task is to nd all frequent itemsets, i.e., to list all combinations of items that are found in a suucient number of examples. A similar task in spirit, but at the opposite end of the complexity scale, is the Inductive Logic Programming (ILP) ap...

Data mining (DM) involves the process of identifying patterns, correlation, and anomalies existing in massive datasets. The applicability DM includes several areas such as education, healthcare, business, finance. Educational Mining (EDM) is an interdisciplinary domain which focuses on DM, machine learning (ML), statistical approaches for pattern recognition quantities educational data. This ty...

The ever-increasing number of fielded Data Mining applications is evidence that the technology works and produces added value in a variety of business areas. Most of the research-lab generated algorithms have found their way under various guises in a number of commercial software packages. When considering the use of Data Mining, the average business user is now faced with a plethora of DM soft...