| Summary: | 碩士 === 南台科技大學 === 資訊管理系 === 98 === As the growing of information technology and internet, the amount of data that an enterprise accumulating is much bigger and bigger. Amounts of data from the visible into the invisible. If use traditional way of searching has not been able to get the useful information quickly and efficiently from a large number of data. So data mining is just to find out the potentially useful information and knowledge. Association rules, Clustering, Classification, and Sequential pattern all the popular way of data mining.
The research is base on Apriori algorithm, modifying Apriori algorithm to be a judgments method of candidate itemsets, and join the concept of the sequence. It to reveal the customers and purchasing behavior’s correlation. And then join the concept of the sequence. It can discover the associations among traveling and purchasing behaviors of customers and overcome the disadvantages of traditional methods. Then redesign the algorithm and enable the algorithm to create all the frequent itemsets after the database updated, without scanning the original database again. When a person is thinking the item about how many amount can be used to sell, and it will provide us very useful information.
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