Fast mining frequent patterns with secondary memory

Kawuu W. Lin, Sheng Hao Chung, Sheng Shiung Huang, Chun-Cheng Lin

研究成果: Conference contribution同行評審


Data mining technology has been widely studied and applied in recent years. Frequent pattern mining is one important technical field of such research. The frequent pattern mining technique is popular not only in academia but also in the business community. With advances in technology, databases have become so large that data mining is impossible because of memory restrictions. In this study, we propose a novel algorithm called Hybrid Mine (H-Mine) to help improve this situation. H-Mine saves a part of the information that is not stored in the memory, and through the use of mixed hard disk and memory mining we are able to complete data mining with limited memory. The results of empirical evaluation under various simulation conditions show that H-Mine delivers excellent performance in terms of execution efficiency and scalability.

主出版物標題Proceedings of the ASE BigData and SocialInformatics 2015, ASE BD and SI 2015
發行者Association for Computing Machinery
出版狀態Published - 7 十月 2015
事件ASE BigData and SocialInformatics, ASE BD and SI 2015 - Kaohsiung, Taiwan
持續時間: 7 十月 20159 十月 2015


名字ACM International Conference Proceeding Series


ConferenceASE BigData and SocialInformatics, ASE BD and SI 2015

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