Data Mining Applications. A Comparative Study for Predicting Students Performance

Doctoral Thesis / Dissertation from the year 2014 in the subject Computer Science - General, course: DOCTOR OF PHILOSOPHY, language: English, abstract: The primary objective of this research is to develop a process to accurately predict useful data from the huge amount of available data using data mining techniques. Data Mining is the process of finding treads, patterns and correlations between fields in large RDBMS. It permits users to analyse and study data from multiple dimensions and approaches, classify it, and summarize identified data relationships. Our focus in this thesis is to use education data mining procedures to understand higher education system data better which can help in improving efficiency and effectiveness of education. In order to achieve a decisional database, many steps need to be taken which are explained in this thesis. This work investigates the efficiency, scalability, maintenance and interoperability of data mining techniques. In this research work, data-results obtained through different data mining techniques have been compiled and analysed using variety of business intelligence tools to predict useful data. An effort has also been made to identify ways to implement this useful data efficiently in daily decision process in the field of higher education in India. Mining in educational environment is called Educational Data Mining. Han and Kamber describes data mining software that allow the users to analyze data from different dimensions, categorize it and Summarize the relationships which are identified during the mining process. New methods can be used to discover knowledge from educational databases. Every data has a lot of hidden information. The processing method of data decides what type of information data produce. In India education sector has a lot of data that can produce valuable information. This information can be used to increase the quality of education. But educational institution does not use any knowledge discovery