ARM-AMO: An Efficient Association Rule Mining Algorithm Based on Animal Migration Optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkAssociation rule mining (ARM) aims to find out association rules that satisfy predefined minimum support and confidence from a given database. However, in many cases ARM generates extremely large number of association rules, which are impossible for end users to comprehend or validate, thereby limiting the usefulness of data mining results. In this paper, we propose a new mining algorithm based on Animal Migration Optimization (AMO), called ARM-AMO, to reduce the number of association rules. It is based on the idea that rules which are not of high support and unnecessary are deleted from the data. Firstly, Apriori algorithm is applied to generate frequent itemsets and association rules. Then, AMO is used to reduce the number of association rules with a new fitness function that incorporates frequent rules. It is observed from the experiments that, in comparison with the other relevant techniques, ARM-AMO greatly reduces the computational time for frequent item set generation, memory for association rule generation, and the number of rules generated

ARM-AMO: An Efficient Association Rule Mining Algorithm Based on Animal Migration Optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkAssociation rule mining (ARM) aims to find out association rules that satisfy predefined minimum support and confidence from a given database. However, in many cases ARM generates extremely large number of association rules, which are impossible for end users to comprehend or validate, thereby limiting the usefulness of data mining results. In this paper, we propose a new mining algorithm based on Animal Migration Optimization (AMO), called ARM-AMO, to reduce the number of association rules. It is based on the idea that rules which are not of high support and unnecessary are deleted from the data. Firstly, Apriori algorithm is applied to generate frequent itemsets and association rules. Then, AMO is used to reduce the number of association rules with a new fitness function that incorporates frequent rules. It is observed from the experiments that, in comparison with the other relevant techniques, ARM-AMO greatly reduces the computational time for frequent item set generation, memory for association rule generation, and the number of rules generated

Diseño de un aplicativo web que recomiende asignaturas electivas a estudiantes de ingeniería industrial de la Pontificia Universidad Javeriana

Las asignaturas electivas buscan el desarrollo integral de los estudiantes, la Pontificia Universidad Javeriana ofrece una gran cantidad de opciones, lo que, usualmente, dificulta el proceso de elección e inscripción de este tipo de asignaturas. Este problema lleva a los estudiantes a retirar las asignaturas o ver asignaturas electivas cuyo contenido no es de su agrado. Estas situaciones no promueven, necesariamente, el proceso de formación integral. Por esta razón, se hace necesaria una herramienta que recomiende asignaturas electivas a los estudiantes, según sus preferencias de aprendizaje. Este problema se va abordar a través de un algoritmo de recomendación mixto que base su predicción en el historial académico de los estudiantes. Se realizó una implementación web de este sistema de recomendación para facilitar el proceso de elección e inscripción de asignaturas electivas, promoviendo, la esencia de la educación integral.Elective subjects seek the student’s integral development, the Pontificia Universidad Javeriana offers a great amount of options, which, usually, makes the election and inscription processes of this kind of subjects more difficult. This problem leads the students to withdraw the subjects or to enroll into subjects whose contents do not reflect their likings. These situations don’t necessarily promote the process of integral formation. This reason makes necessary a tool that recommends elective subjects to students, according to their learning preferences. And the problem will be addressed through a hybrid recommender algorithm that bases its prediction on students' academic records. A web implementation of this recommendation system was made to facilitate the process of electing and registering elective subjects, promoting the essence of integral education.Ingeniero (a) IndustrialPregrad