Data mining tasks such as clustering and classification have proved to highly impact various fields such as business, including the banking sector, as well as medicine, including the radiology sector. As the decision‐making process is critically dependent on the availability of high‐quality information presented in a timely and easily understood manner, the successful application of efficient data mining approaches is a great support for achieving the required target in the available time. This study presents an enhancement for the Iterative Dichotomiser 3 (ID3) classification decision tree algorithm based on two related approaches, namely, data partitioning and parallelism. The study applied the proposed algorithm in the banking and radiology sectors; as data have been classified to the defined fields’ clusters, the processing time and the results’ accuracy parameters have been compared with the ID3 algorithm and have proved an enhancement in both parameters. WIREs Data Mining Knowl Discov 2016, 6:70–79. doi: 10.1002/widm.1177 This article is categorized under: Algorithmic Development > Hierarchies and Trees Application Areas > Government and Public Sector Application Areas > Health Care Technologies > Classification

Educational data mining is concerned with the development methods for exploring the unique types of data that come from the educational context. Furthermore, educational data mining is an emerging discipline that concerned with the developing methods for exploring the unique types of data that come from the educational context. This study focuses on the way of applying data mining techniques for higher education system by using the most common techniques on most common application called Moodle system in education system. There are an increasing numbers of researches that interest in using data mining in education system. The proposed system for Higher Educational Data Mining System (HEDMS) is concerned with the developing methods that discover useful knowledge from data that extracted from educational system. The data collated form historical and usage data reside in the databases of educational institutes. The proposed system helps to get sufficient results which consist of several steps in our case study starting with collected data, pre-processing, applying data mining techniques and visualization results. We collected students' data from Moodle database.

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The Demand for healthcare IT and its analytics increases in the last few years. To improve quality of care (e.g., ensuring that patients receive the correct medication) which will help to improve the efficiency of clinical quality and safety, operations.The Nature of the medical field is rich with information where there's a variety and abundance of data but untapped in a correct and effective manner to get the right knowledge. and therefore, the most serious challenge facing this area is the quality of service provided which means to make the diagnose in a proper manner at a timely manner and provide appropriate medications to patients because Poor diagnosing can lead to serious consequences which are unacceptable. And because there is a lack of effective analysis tools to discover hidden relationships and trends in data, so Health information technology has emerged as a new technology in health care sector in a short period by utilizing Business Intelligence 'BI' which is a data-driven Decision Support System. Which Was developed from 1990s to now, and gradually become one of the most important information systems applied in any sector. BI enables to deal with huge amount of data and extract useful knowledge to support decision making. Data mining 'DM' is a kind of data processing technology which can be regarded as a part of the BI system, but it can be also considered as an independent and integrated technology which can treat mass data and extract hidden relationships from it.This introduction highlights the main importance of how to apply the business intelligence applications using data mining techniques to help medical professionals in healthcare sector rapidly diagnosing and predicting diseases of any patients not only this but also detecting the disease complications on the patient which will decrease the overall cost of expenditure that the country paid, briefly this is the central research idea which address the motivation for doing this research.

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