An Integrated Framework with Interactively and Adaptively Mining Association Rules

• Main Authors: Sheng-hua Yang, 楊昇樺
• Other Authors: Hung-Pin Chiu
• Format: Others
• Language: zh-TW
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/43916428048102496673

碩士 === 南華大學 === 資訊管理學研究所 === 91 === 　　In the past, the traditional model for knowledge discovery process (KDP) was usually simplified to facilitate the proceeding of the research, or only a single sub-problem in the KDP was solved at a time. Recently, the researchers and practitioners have realized the limitations of the traditional model and felt the need for standardization of the KDP. It is significantly meaningful that all the problems in the KDP should be considered and solved together. This is so called the Intension Mining Model for KDP. In this study, we discuss the extended model for association rule mining that is one of the popular research areas in data mining. A number of techniques and algorithms have been proposed to mine the association rules from different aspects, respectively, such as the on-line mining, the incremental mining, the interestingness problems, and so on. But few of researches have attempted to solve these problems in an integrated way. Therefore, we design a new algorithm, namely the QARM (Query-based Association Rule Miner), based on our previously proposed MUM (Multi-layer Update Miner), to construct an integrated framework, namely the QAMUM (Query-based Adaptive MUM), for mining association rules efficiently. Many experiments were conducted to verify the practicability and feasibility of the proposed approach.