Data Mining

The fuzzy logic data mining method is a method that bases its concept on probabilities. The use of fuzzy logic modeling depends much on the problem at hand and the data set. The main concept of having a data mining concept is to have useful information derived from large amounts of data (Tan, et al., 2019). The advantages of using the fuzzy logic data mining are that it provides an effective and flexible approach whenever there is vagueness and uncertainty when classifying data. The fuzzy modeling method is the best model to classify students based on a large number of applicants to get into the university. The reason is that there will be no uncertainties on where to place each applicant.

Clustering simplifies the classification of huge data in that the information obtained from each applicant will be clustered to the right group. From the situation, the three clusters were efficient; one, there were criteria to key in each applicant to be admitted to the university. Secondly, the lack of the requirements to be admitted classified the applicants as rejected in the university (Cárdenas, Zapata-Zapata, & Kim, 2020). The third cluster was the group of yet to be admitted. In the third cluster of those who should be admitted, they should have let all the required criteria. Secondly, it is my obligation as a staff in the admission office to make the admissions faster to avoid chaos and misunderstanding. To avoid uncertainty and vagueness, I would rank the students in a way that those who passed the criteria perfectly got the chance first.

References

Cárdenas, E. L. M., Zapata-Zapata, A. D., & Kim, D. (2020). Modeling dark fermentation of coffee mucilage wastes for hydrogen production: Artificial neural network model vs. fuzzy logic model. Energies, 13(7). https://doi.org/10.3390/en13071663

Tan, P., Steinbach, M., Karpatne, A., & Kumar, V. (2019). Introduction to data mining. 2 nd ed. New York: Pearson