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[求助]
摘要漢譯英(計算機數(shù)據(jù)挖掘方面)
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為了應對實際應用中經常遇到的頻繁更新的數(shù)據(jù),一種與時機判定相結合的關聯(lián)規(guī)則增量更新算法開始被人們廣泛研究。丁虎在基于數(shù)據(jù)倉庫的關聯(lián)規(guī)則抽樣算法中給出了一種基于完全頻繁項集的關聯(lián)規(guī)則差異度判定方法,它能夠充分表達數(shù)據(jù)集更新前后關聯(lián)規(guī)則的變化,減少關聯(lián)規(guī)則更新的次數(shù),因此得到比較廣泛的應用,然而這種算法在計算新增數(shù)據(jù)頻繁項集時不夠準確,本文將提出一種DFUP算法,該算法通過引入一個動態(tài)數(shù)據(jù)庫來解決上述算法的不足。 關鍵字:關聯(lián)規(guī)則;增量更新;時機判定;關聯(lián)規(guī)則差異度 |
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In order to deal with the frequently updating of data in practice, association rules incremental updating algorithm combining with time judgment begun to be widely studied. DingHu gave an association rule difference degree judgment method based on the completely frequent itemsets. This method can fully express the changes of association rules before and after the dataset updating, and reduce the times of the updating of association rules, therefore, this method was widely used. However, this method is not very accurate when calculating the new frequent itemsets. DFUP algorithm is proposed in this paper, and this algorithm solves the problem of the above algorithm by introducing a dynamic database. Keywords: association rule, incremental updating, time judgment, association rule difference degree |
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