Applying data mining (DM) in education is an emerging interdisciplinary research field also known as educational data mining (EDM). It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain ins...

The growing advances in mobile devices, processing power, display and storage capabilities, together with competitive market has enabled information technology to be more affordable and available to almost everybody around the world. Moreover, with the advent of wireless communications and mobile computing, another type of wireless communications, called Mobile Ad hoc NETworks (MANETs), came in...

Abstract— ERP systems contain huge amounts of data related to the actual execution of business processes. These systems have a particular way of recording activities which results in an unclear display of business processes in event logs. Several works have been conducted on ERP systems, most of them focusing on the development of new algorithms for the automatic discovery of business processes...

data sanitization is a process that is used to promote the sharing of transactional databases among organizations and businesses, it alleviates concerns for individuals and organizations regarding the disclosure of sensitive patterns. it transforms the source database into a released database so that counterparts cannot discover the sensitive patterns and so data confidentiality is preserved ag...

Objective This work leverages spatio-temporal data mining (ST-DM), the MiSTIC (Mining Spatio-Temporally Invariant Cores)[1,6] method for infectious disease surveillance, by identifying a) Extent of spatial spread of disease core regions across populations-scale of disease prevalence b) Possible causes of the observed patterns-for better prediction, detection & management of infectious disease &...

ion (TA) across multiple parameters for multiple patients to enable mining of multi-dimensional temporal data 3. The TAMDDM framework can be applied in a neonatal context 4. The TAMDDM framework can support null hypothesis testing 5. The hypotheses generated by the framework can be used by a real-time event stream processor analysing the current condition of babies in a Neonatal Intensive Care ...

Massive Open Online Courses (MOOCs) have gained tremendous popularity in the last few years. Thanks to MOOCs, millions of learners from all over the world have taken thousands of high-quality courses for free. Putting together an excellent MOOC ecosystem is a multidisciplinary endeavour that requires contributions from many different fields. Artificial intelligence (AI) and data mining (DM) are...

The accumulating amounts of data are making traditional analysis methods impractical. Novel tools employed in Data Mining (DM) provide a useful alternative framework that addresses this problem. This research suggests a technique to identify certain patient populations. Our model examines the patient population and clusters certain groups. Those subpopulations are then classified in terms of th...

� Data mining (DM) is a widely adopted methodology for the analysis of large datasets which is on the other hand often overestimated or incorrectly considered as a universal solution. This statement is also valid for clinical research, in which large and heterogeneous datasets are often processed. DM in general uses standard methods available in common statistical software and combines them int...

Nowadays, a large number of data mining (DM) techniques are available for data analysis and prediction in medicine. Few literatures are available that provide a comprehensive view which give a guideline to use the DM techniques in different medical areas (domain). This review provides a comprehensive view of the state of the art of single DM and integrated case-based reasoning (CBR) and DM tech...