Mining Interested Information from Large Database
碩士 === 輔仁大學 === 資訊工程學系 === 88 === The destination of mining association rules is to discover the associative purchasing behaviors from each transaction of most customers. Mining sequential patterns is to discover the sequential purchasing behaviors from a large amount of the transactions of customer...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2000
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| Online Access: | http://ndltd.ncl.edu.tw/handle/98504799205938414053 |
| Summary: | 碩士 === 輔仁大學 === 資訊工程學系 === 88 === The destination of mining association rules is to discover the associative purchasing behaviors from each transaction of most customers. Mining sequential patterns is to discover the sequential purchasing behaviors from a large amount of the transactions of customers. Both of these are to discover the useful information. Because the transactions are increased and updated frequently, the system must discover all of the association rules and sequential patterns in a period of time. Besides, browsing through all of the information is not efficient if the users are only interested in part of the information. For these reasons, we design a data mining language for the users to define what they are interested. Then the system can look for the interested information rapidly according to the definition defined by the users.
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