Association Rule Mining for Specific New Course
Nhabhat Chaimongkol · NRCT Data Center · 2010
Most language schools devote a significant portion of their budget on new courses to distinguish their school from their competitors and to increase the number of students. The schools should specify courses that fulfill the needs. This will raise the competitiveness of the schools. Also the schools will earn higher loyalties and profits because of the increase of new students. This article proposes a Mining Course Map (MCM) algorithm for investigating on the relationships among demands, type of course and transaction records. MCM is a modified association rule analysis based-on FP-growth algorithm. For comparison study, the proposed method was compared with Association Rule Miner And Deduction Analysis (ARMADA). The results show that the execution time of MCM is less than ARMADA which means that MCM is more efficient than the ARMADA. In addition, the results show that different knowledge and rules can be extracted from students to specify new courses for new and old members. This paper suggests that the school should extract knowledge from student demands. The knowledge can be used to manage new courses properly.