Knowledge Discovery in Databases (KDD) is an automatic, exploratory analysis and modeling of large data repositories. KDD is the organized process of identifying valid, novel, useful, and understandable patterns from large and complex data sets. Data Mining (DM) is the core of the KDD process, involving the inferring of algorithms that explore the data, develop the model and discover previously...

Knowledge Discovery in Databases (KDD) is a highly complex, iterative and interactive process that involves several types of knowledge and expertise. In this paper we propose to support users of a multi-view analysis (a KDD process held by several experts who analyze the same data with different viewpoints). Our objective is to enhance both the reusability of the process and coordination betwee...

Decision support focuses on developing systems to help decision-makers solve problems. It provides a selection of data analysis, simulation, visualization and modeling techniques, and software tools such as decision support systems, group decision support and mediation systems, expert systems, databases and data warehouses. Data mining deals with finding patterns in data that are by user-defini...

association rules are among important techniques in data mining which are used for extracting hidden patterns and knowledge in large volumes of data. association rules help individuals and organizations take strategic decisions and improve their business processes. extracted association rules from a database contain important and confidential information that if published, the privacy of indivi...

Knowledge Discovery and Data Mining are powerful automated data analysis tools and they are predicted to become the most frequently used analytical tools in the near future. The rapid dissemination of these technologies calls for an urgent examination of their social impact. This paper identifies social issues arising from Knowledge Discovery (KD) and Data Mining (DM). An overview of these tech...

The results of data Mining (DM) such as association rules, classes, clusters, etc, will be readily available for working team. So the mining will penetrate the privacy of sensitive data and makes the stolen of the knowledge resulted much more easily. The main objective of the proposed system is preserving the privacy of data mining, that will done by developing algorithms for modifying, encrypt...

The direct-marketing business process applied in Slovenian publishing company was inefficient because of inadequate procedure used to create a list of potential customers for a marketing campaign. Considering the nature of the problem, data mining was selected to solve the problem. The company’s direct marketing business process was renovated based on the CRISP-DM methodology and by using data ...

This chapter describes the convergence of two influential technologies in the last decade, namely data mining (DM) and the Semantic Web (SW). The wide acceptance of new SW formats for describing semantics-aware and semistructured contents have spurred on the massive generation of semantic annotations and large-scale domain ontologies for conceptualizing their concepts. As a result, a huge amoun...

Most knowledge discovery processes are biased since some part of the knowledge structure must be given before extraction. We propose a framework that avoids this bias by supporting all major model structures e.g. clustering, sequences, etc., as well as specifications of data and DM (Data Mining) algorithms, in the same language. A unification operation is provided to match automatically the dat...