Data Mining

Fuzzy logic provides critical analysis for problems both in numeric and linguistic data using the membership function. To analyze the problem, the third cluster provides an alternative that is between admitted and rejected students in the university. The application can be analyzed using the fuzzy logic steps that include rule base, fuzzification, inference engine, and defuzzification. Fuzzification involves converting the crisp set to a fuzzy set to ensure further processing of rules (Bělohlávek et al., 2017). The third cluster can involve ranking the students according to previous results, disability, and personal sponsorship, and three rules offer for a more accessible analysis of the applicants. The rule base provides for the use of if-then condition and mainly used to reduce the number of fuzzy sets (Boubellouta et al., 2019). One rule can be If one has passed and willing to pay, then register or if one is disabled and attend minimum grades, then register.  The rule can further show if a student is with minimal results and not disabled, do not register.

The inference engine controls the grade of the match of the fuzzy input and the rule. When the rules are combined, the control action is realized for the formation of a result, such as in the third cluster involving the registering of students. The results are matched according to input provided and how it matches with rules set (Shurbin et al., 2019). Lastly, defuzzification provides for a crisp value for the result, which is converted from a fuzzy set. The initial input provided is ranked according to the significance of value rather than the allocation of space.

The process provides accurate reasoning, which is the only reasoning for the problem making the solution most appropriate. The students register to meet the criteria set for ranking for the most viable to the lest viable but with the condition of accepting disable showing minimum progress.

References

Bělohlávek, R., Dauben, J. W., & Klir, G. J. (2017). Fuzzy logic and mathematics: a historical perspective. Oxford University Press.

Boubellouta, A., Zouari, F., & Boulkroune, A. (2019). Intelligent fuzzy controller for chaos synchronization of uncertain fractional-order chaotic systems with input nonlinearities. International Journal of General Systems, 48(3), 211-234.

Shurbin, O., Kondratenko, G., Sidenko, I., & Kondratenko, Y. (2019). Computerized System for Cooperation Model’s Selection based on Intelligent Fuzzy Technique. In 1st International Workshop on Information-Communication Technologies & Embedded Systems (Vol. 2516, pp. 206-217).