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DATA MINING TECHNIQUES IN ANALYSIS
OF STUDENT COURSE OF STUDY
n recent years, the technology of
database has become more advanced where large amount of data is required to be
stored in the databases. Data mining then attract more attention to extract
valuable information from the raw data that institution can use for decision-making
process. It applies modern statistical and computation technologies to expose
useful information hidden within the large database to remain competitiveness
among educational field, the institution need deep and enough knowledge for a
better assessment, evaluation, planning and decision-making. Data mining helps
institution to use their current reporting capabilities to discover and
identity the hidden patterns in database and hence can be used to predict
performance of the student.
Data mining can be viewed as a
result of the natural evolution of information technology because before 1960
when database and information technology had not evolved, analysis of data was
basically the primitive file processing which would not give the appropriate useful
information despites the huge amount of time consumed. The evolutionary path of
data mining has been witnessed in the database industry in the development of
the following database and information technology.
Data collection and data creation
Data management (including data
warehouse and data preparation)
Data analysis and understanding
(involving data mining and data interpretation)
Moreover, data mining is also known
as knowledge discovery in large database (KDD). Consequently, data mining
consist of more than collecting and managing data; it also includes analysis
and predictions. Important decision are often made based not on the information
rich data stored in database but rather on decision maker̢۪s institution,
simply because maker does not have the tools to extract the valuable knowledge
embedded in the vast amount of data.
1.2 Statement of the problem
It is not feasible for people to
analyze great amounts of data without the assistance of appropriate
computational tools. Therefore, the development of tools of an automatic and
intelligent nature becomes essential for analyzing, interpreting, and
correlating data in order to develop and select strategies in the context of
each application. To serve this new context, the area of Knowledge Discovery in
Databases (KDD), came into existence with great interest within the scientific,
industrial, and commercial communities. The popular expression â€Å“Data
Mining†is actually one of the stages of the Discovery of Knowledge in
Databases. The term â€Å“KDD†was formally recognized in 1989 in reference to
the broad concept of procuring knowledge from databases. One of the most
popular definitions was proposed in 1996 by a group of researchers. According
to Fayyad, et al. (1996): â€Å“KDD is a process with many stages, non-trivial,
interactive, and iterative, for the identification of comprehensible, valid,
and potentially useful patterns from large data setsâ€. It is of utmost desire
to extract valuable information from large databases.
This research work therefore
addresses the intelligent prediction of students̢۪ course of study in higher
institution based on the historical student academic data. This will facilitate
better performance of students in high institutions.
1.3 Aim and Objectives of the Project
1.3.1 Aim
The aim of the research work is to
develop a computer application software that will be able to predict student
course of study in higher institution using classification algorithm.
 1.3.2 Objectives
The following are the set of objectives
addressed by the project work:
To develop and populate student
academic database
To develop a computer application
program that will be able to mine knowledge from the students̢۪ academic
database using Classification algorithm.
To predict student  course
of  study according to their Post UTME cutoff.
To reduce the rate at which student
admission is fortified.
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