| Summary: | 碩士 === 銘傳大學 === 資訊工程學系碩士班 === 101 === Mining sequential patterns is to find the sequential purchasing behaviors for most of the customers, which only considers the number of the customers with the behaviors in a customer transaction database. Mining high utility sequential patterns considers both of the profits and purchased quantities for the items, which is to find the sequential patterns with high benefits for the business. The previous researches roughly defined the utility of a sequence contributed by a customer, such that the generated patterns are not really high utility. Moreover, the previous approaches need to generate a large number of the candidates and waste a lot of time to check if they are high utility. Therefore, in this paper, we consider the actual purchasing behaviors for the customers and exactly define the high utility sequential patterns. Besides, we propose an efficient algorithm for mining high utility sequential patterns which can significantly reduce the number of the candidates. For another purpose, a various version of our algorithm for mining the top K sequential patterns with the highest utilities is also purposed. The experimental results show that our algorithm significantly outperforms the previous approach for mining high utility sequential patterns.
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