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• Main Authors: Li-mei Lee, 李麗美
• Other Authors: Li-chen Cheng
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/38983047775092007452

碩士 === 東吳大學 === 資訊管理學系 === 100 === Traditional associative classification is used to search frequent patterns at the balance datasets. However, most real life datasets are imbalance. To discover special rare patterns from imbalance dataset is an important job. Currently, the freeway becomes the main transportation route at Taiwan. Because of the high speed and heavy traffic, accidents at highway would cause more serious injuries than other roads. The serious injury accidents are very small part among the accident data. The impact factors of these special cases are the most important issue. This study proposes a methodology to explore the most significant reasons for serious accidents. The framework combines the associative classification method with the emerging patterns mining to discover rare and serious incidents. The weight of each accident is adjusted by the severity of accident. Since the rare items can be discovered by the proposed formula of calculation support. The results of an experiment that was conducted on a real accidents data demonstrated the efficacy of the proposed approach. After analyzing these accidents, we provide some suggestions.