Mining Enrolment Data Using Predictive and Descriptive Approaches

In recent years, the technology of database has become more advanced where huge amount of data is required to be stored in the databases, and the wealth of information hidden in those datasets has been realized by business people as a useful tool for making business strategic decisions. Data mining then attract more attention as it promises to extract valuable information from the raw data that businesses can use to increase their profitability through a profitable decision-making process. Data mining is used to describe knowledge in databases; it is a process of extracting and identifying useful information and subsequent knowledge from databases using statistical, mathematical, artificial intelligence and machine learning technique (Efraim et al., 2007). Data mining applies modern statistical and computational technologies in its quest to expose useful pattern hidden within the large databases. It has proved itself as a powerful tool, capable of providing highly targeted information to support decision-making and forecasting for scientific, physiological, sociological, the military and business decision making. The predictive power of data mining comes from its unique design by combining techniques from machine learning, pattern recognition, and statistics to automatically extract concepts, and to determine the interrelations and patterns of interest from large databases (Edelstein, 1997). To date, higher educational organizations are placed in a very high competitive environment and are aiming to get more competitive advantages over the other business competitors. These organizations should improve the quality of their services and satisfy their customers such as industries and government agencies. To remain competitiveness among educational field, these organizations need deep and enough knowledge for a better assessment, evaluation, planning, and decision-making. Majority of the required knowledge that has been stored in the educational organization’s database can be extracted from the historical and operational data. Therefore, one approach to effectively tackle the student and administration challenges is through the analysis and presentation of data, or data mining. Data mining helps organizations to use their current reporting capabilities to discover and identify the hidden patterns in databases. The extracted patterns are then used to build data mining models, and hence can be used to predict performance and behaviour with high accuracy. As a result of this insight, universities are able to allocate resources more effectively. Data mining may, for example, give a university the information necessary to take action before students quit their study, or to efficiently assign resources with an