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frequent itemset中文

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用"frequent itemset"造句"frequent itemset" in a sentence"frequent itemset"怎麼讀

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  • 頻繁項集

例句與用法

  • A bit string array - based mining algorithm for maximum frequent itemset
    基于位串數組的最大頻繁項目集挖掘算法
  • The problem of fuzzy constraint in frequent itemset mining is studied
    摘要研究頻繁項集挖掘中的模糊約束問題。
  • Most of the previous studies adopt an apriori - like heuristic , that is , any subset of frequent itemset is frequent itemsets
    目前絕大多數頻繁集產生算法都是采用類似apriori算法的思想即一個頻繁集的任意子集都是頻繁集。
  • Aims at the inherent fault of the apriori algorithm , analyzes and realizes the fp - growth which does not generate candidate mining frequent itemset
    針對apriori算法的固有缺陷,對不產生候選挖掘頻繁項集方法- - fp - growth頻集算法進行分析并加以實現。
  • Data mining , association rule , frequent itemset , sample error , multi - scaling sampling references 1 evfimievski a , srikant r , agrawal r , gehrke j . privacypreserving mining of association rules
    已有的研究表明:根據數據庫的特性,動態的選取樣本大小進而獲取可接受的近似關聯規則的自適應取樣方法是解決上述問題的一種好方法。
  • In this paper , we propose a novel approach using sentential frequent itemset , a concept comes from association rule mining , for text classification , which views a sentence rather than a document as a transaction , and uses a variable precision rough set based method to evaluate each sentential frequent itemset s contribution to the classification
    為了解決這一問題,參考目前的數據挖掘領域的工作,提出了一個文檔數據庫模型,即將每一篇文檔映射為一個文檔數據庫,文檔中的每個句子看作數據庫中的一個交易,每一個詞看作一個項目。
  • The paper adopts the design of the ceedm and the association rule mining technology which is charged by the author , studies the important notation , method and strategy of data mining technologies , discusses the application and realize of association rule mining technology emphatically , and aims at the inherent fault of the apriori algorithm , analyzes and realizes the fp - growth which does not generate candidate mining frequent itemset
    本文結合數據挖掘系統ceedm的設計與系統中作者負責實現的關聯規則挖掘技術部分,對數據挖掘技術中的一些重要的概念、方法和策略進行研究,集中討論了關聯規則挖掘技術在ceedm系統中的應用與實現,并針對apriori算法的固有缺陷,對不產生候選挖掘頻繁項集方法- - fp - growth頻集算法進行分析并加以實現。
  • The result shows that the time complexity of algorithm is linear with the increment of transaction if the average length of transaction and frequent itemsets is invariable , but it is inefficient to the increament of item average length ( including transaction length and frequent itemset length )
    結果表明:在事務平均長度和頻繁數據項集一定條件下事務規模對算法的時間復雜性影響是線性的;但算法卻不能很好解決數據項長度(事務和頻繁數據項平均長度)增大對其性能的影響。
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