Human capital is of a high concern for companies’ management where their most interest is in hiring the highly qualified personnel which are expected to perform highly as well. Recently, there has been a growing interest in the data mining area, where the objective is the discovery of knowledge that is correct and of high benefit for users. In this paper, data mining techniques were utilized to...

Data mining (DM) techniques are being increasingly used in many modern organizations to retrieve valuable knowledge structures from organizational databases, including data warehouses. An important knowledge structure that can result from data mining activities is the decision tree (DT) that is used for the classi3cation of future events. The induction of the decision tree is done using a super...

Data mining (DM) research has successfully developed advanced DM techniques and algorithms over the last few decades, and many organisations have great expectations to take more benefit of their data warehouses in decision making. Currently, the strong focus of most DM-researchers is still only on technology-oriented topics. Commonly the DM research has several stakeholders, the major of which ...

Since formulation of Inductive Database (IDB) problem, several Data Mining (DM) languages have been proposed, confirming that KDD process could be supported via inductive queries (IQ) answering. This paper reviews the existing DM languages. We are presenting important primitives of the DM language and classifying our languages according to primitives‟ satisfaction. In addition, we presented lan...

The rate at which organizations are acquiring data is getting out of proportion and managing such data so as to infer useful knowledge that can be put to use is increasingly becoming important and challenging. Data Mining (DM) is one such relatively recently technology that has emerged that is employed in inferring useful knowledge that can be put to used from a vast amount of data. This paper ...

Data Mining(DM) is the process of extracting implicit, valuable, and interesting information from large sets of data. As huge amounts of data have been stored in tra c and transportation databases, data warehouses, geographic information systems, and other information repositories, data mining is receiving substantial interest from both academia and industry. The Twin-Cities tra c archival stor...

Data mining (DM) involves the use of a suite of techniques that aim to induce from data, models that meet particular objectives. DM algorithms are built on a range of techniques, including information theory, statistics, linear and non-linear models, AI, meta-heuristics. Within the context of data analysis methods, data mining can be considered to be an exploratory knowledge discovery approach ...

The principal aim of this project is to set up new mathematical models together with efficient associated algorithms that allow the incorporation of some elements of the Formal Concept Analysis (FCA), of the Mathematical Morphology (MM), of the Aggregation Functions Theory (AFT) and of the Fuzzy Relation Theory (FRT) in the following phases included in the Knowledge Discovery (KD) processes: da...

industrial-scale r&d it projects depend on many sub-technologies which need to be understood and have their risks analysed before the project can begin for their success. when planning such an industrial-scale project, the list of technologies and the associations of these technologies with each other is often complex and form a network. discovery of this network of technologies is time con...

Nowadays, the necessity and usefulness of the field of Data Mining (DM) and Knowledge Discovery in Databases (KDD) are largely established by both the scientific and industrial communities; and a number of real applications have already been developed in domains ranging from space data to financial analysis [1]. However, the need for scaling up DM algorithms is a natural requirement of the more...