A Search Algorithm in Distance Education
The development of data mining systems has received a great deal of attention in recent years. One of the challenges in developing data mining systems is to integrate and coordinate existing data mining applications in a seamless manner so that cost-effective systems can be developed without the need of costly proprietary products. The popularity of distance education has grown rapidly over the last decade in higher education, yet many fundamental teaching-learning issues are still in debate. This paper presents an approach for classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. In this paper we take advantage of the genetic algorithm (GA) designed specifically for discovering association rules. We propose a novel spatial mining algorithm, called ARMNGA(Association Rules Mining in Novel Genetic Algorithm), Compared to the algorithm in Reference2,the ARMNGA algorithm avoids generating impossible candidates, and therefore is more efficient in terms of the execution time.
data mining genetic algorithm association rules distance education
GAO Li ZHENG Shijue SHU Wanneng SU Ying
Department of Computer Science Hua Zhong Normal University Wu Han, 430079, China
国际会议
武汉
英文
1210-1212
2007-07-25（万方平台首次上网日期，不代表论文的发表时间）