| Summary: | 碩士 === 元智大學 === 資訊管理學系 === 97 === Database is the common tool for data archive nowadays. It is valuable to obtain useful data and create new knowledge using data mining technologies to analyze and integrate the tremendous and ever-increasing data. The relational rule method which finds relations in database is the most widespread among the data mining technologies. However, it takes repetitive checking and scanning of the database using traditional relational rule algorithm, and consequently limited the data mining efficiency.
This research adopted the FP-Growth as basis and integrated fuzzy partition to propose the Fuzzy Frequent Pattern Growth (FFP-Growth) algorithm that integrated the FP-Growth double database compression and scanning and the fuzzy partition rule to determine the fuzzy group for each item. The feature of this algorithm is that the generate frequent patterns can be created after updating trade database, without re-scanning the original database and therefore improve the data mining efficiency.
The fuzzy association rules were investigated from the quantitative trade data in the third part of the research. In addition, due to the fast increase of internet applications, the useful web browse style was explored from web server browsing records. The explored knowledge can be used in marketing and management decision making, and create new business chances.
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