Association rules mining for knowledge management: a case study of library services
作者： Chu Chai Henry Chan , Ming-Hsiu Lee , Yun-Chiang Kwang
DOI:
关键词: Core (game theory) 、 Knowledge management 、 Tracking (education) 、 Apriori algorithm 、 Preference 、 Association rule learning 、 Data science 、 Association (object-oriented programming) 、 Ranking 、 Engineering 、 Work (electrical)
摘要: Data mining has been applied successfully in a lot of business communities for understanding and tracking behavior individual or certain groups. To realize the actual needs college students, this study proposes using data to discover association rules library database. This major advantage is provide novel mechanism by problem-solving oriented approach rather than technical concept done most previous researches. We apply Apriori algorithm as core methodology implementing mining. prove proposed methodology, an empirical case conducted find between different users' demands. Moreover, knowing about students' preference, work finds searches top ten ranking books students three colleges. One interesting finding that have patterns. conclusion can give guideline studied understand background students. Following finding, university offer suitable services future.
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