Improved Rule Discovery Using FP Growth Algorithm in Educational Data Mining | Original Article
In the educational data, the secret information can be extorted from the great quantity of data. This data can be used for the development in the educational system and making it high class education. The methods of data mining are useful for the extraction of the reality of the educational system. FP Growth is a regular unremitting continuous example mining, which produces visit itemset without candidate innovation. It impacts usage of tree based to structure. The learning methodologies of the academic for particular university with their related information are used. Each student’s performance is evaluated from the database and it should be that reliable to resist the changes in the academic record. After that we have changed over the general arrangement into a changed relationship for appropriateness of utilizing FP Growth. In this paper, the useful rules are generated for showing the relationship among different number of attributes. FP Growth used in this paper for the appealing rules generation and extort the efficient rules. These rules are helpful to show the achievement of the each and every student. The teaching methodologies are taken into consideration to achieve the better academic of the students and it can be generated by taking different parameters.