This study aimed to indicate the level of interest in the application of concepts and
data mining tools in the management of banking operations areas and the interest
components of the environment and the application of the concepts of data mining
tools in the management of banking operations in commercial banks of Jordan. To
achieve these goals, the researcher used the descriptive analytical approach based on
the questionnaire distributed to members of the community study. The researcher
found that the percentage of interest among members of the community study on the
application of the concepts of data mining operations the management of banking,
was high in general, where the arithmetic mean is generally equal to (4.005). And
that the order of fields that may be seen when you search in the application of the
concepts of data mining and addressed by this study, have been of importance and
level of interest by the members of the population of the study, as follows: the
working environment of knowledge with information technology "has obtained the
highest average, was the average the arithmetic of this axis is equal to (4.02),
followed by the center of "opportunities to enhance knowledge systems with the
development environment systems research and retrieval of data.
Educational data mining aims to study the available data in the educational field and extract the hidden knowledge from it in order to benefit from this knowledge in enhancing the education process and making successful decisions that will improve th
e student’s academic performance. This study proposes the use of data mining techniques to improve student performance prediction. Three classification algorithms (Naïve Bayes,J48, Support Vector Machine) were applied to the student performance database, and then a new classifier was designed to combine the results of those individual classifiers using Voting Method. The WEKA tool was used, which supports a lot of data mining algorithms and methods. The results show that the ensemble classifier has the highest accuracy for predicting students' levels compared to other classifiers, as it has achieved a recognition accuracy of 74.8084%. The simple k-means clustering algorithm was useful in grouping similar students into separate groups, thus understanding the characteristics of each group, which helps to lead and direct each group separately.
