| Título: | An Improved Apriori Algorithm for Association Rules |
| Autores: |
Liu, Xingli; Heilongjiang Institute of Science and Technology Liu, Huali; Heilongjiang Institute of Science and Technology |
| Fecha: | 2013-11-01 |
| Publicador: | TELKOMNIKA: Indonesian journal of electrical engineering |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| Tema: | Apriori Algorithm; Association Rules; Curriculum knowledge Correlation |
| Descripción: | According Apriori algorithm characteristic achieve its improvement and apply it to the knowledge correlation of the curriculum in sumulation experiment. Firstly, it is mainly by simplifying the binary storage method to change data in the database, and then to get the largest frequent itemsets.The experiment results showed that the improved algorithm obviously improve the efficiency ;secondly ,establish a new database to simulate applied experiment ,consisted of student achievement of various knowledge points in the computer programming course,and then using this optimized algorithm to found the course knowledge frequent itemsets in a database, which is closely interrelated knowledge points mainly by setting up different minimum support value to get various frequent itemsets .According to these frequent itemsets of the course it can be applied to reestablish a new course knowledge system to further improve the teaching quality, this method can also be achieved the knowledge system reform of other course or course group. |
| Idioma: | Inglés |