頻繁項集 的英文怎麼說

中文拼音 [bīnfánxiàngjí]
頻繁項集 英文
frequent itemset
  • 頻 : Ⅰ形容詞（次數多） frequent Ⅱ副詞（屢次） frequently; repeatedly Ⅲ名詞1 [物理學] （物體每秒鐘振動...
  • 繁 : 繁名詞（姓氏） a surname
  • 項 : Ⅰ名詞1 （頸的後部） nape （of the neck） 2 （款項） sum （of money） 3 [數學] （不用加、減號連接...
  • 集 : gatherassemblecollect
  • 頻繁 : frequently; often
  1. Text classification using sentential frequent itemsets

    使用句子級頻繁項目集的文本分類方法
  2. This paper presents the directed itemsets graph to store the information of frequent itemsets of transaction databases, and puts forward the trifurcate linked list storage structure of directed itemsets graph, and provides the mining algorithm of frequent closed itemsets based on directed itemsets graph

    摘要利用了有向項集圖來存儲事務數據庫中有關頻繁項集的信息，提出了有向項集圖的三叉鏈表式存儲結構和在於有向項集圖的頻繁閉項集挖掘演算法。
  3. In addition, comparing to direct using fp _ growth algorithms, this algorithm has no need to expand negative item to original database, and construc or destruct additional data structures, which only make some changes on the original frequent pattern tree, so it has certain advantages in time and space costs

    除此之外，該演算法與直接使用fp _ growth演算法挖掘含負項目的頻繁項集演算法相比，無需對原始數據庫進行負項目的擴展，也不用再構造並銷毀額外的數據結構，只需在原始的頻繁模式樹上修改，在時間和空間的開銷上都具有一定的優勢。
  4. We apply the divide - and - conquer strategy to this issue and develop algorithm dciua. it not only adopts the merits of divide - and - conquer method but also fully utilizes information of the original frequent itemsets under the old minumum support. so dciua is more efficient than the direct application of apriori under the new minimum support

    我們把分治策略的思想應用到更新問題上，設計了演算法dciua ，既利用了分治方法的優點，同時又充分利用已有的頻繁項目集的信息，因而演算法是有效的。
  5. 4 ) we introduce a classical algorithm which can extract implication rules based on concept lattice. the algorithm builds lattice incrementally and updates the set of rules simultaneously. we modify the structure of lattic to meet our requirement of finding frequent itemsets

    （ 4 ）介紹一種經典的基於概念格的提取蘊涵規則的演算法，該演算法增量式建格，同時更新規則集，我們根據需要對格結構進行相應修改，從而可以增量式獲取頻繁項集。
  6. This paper analyses some of the existed algorithms of mining association rules, and proposes an new algorithm ffc based on the algorithm aprioritid and close which is used to mine close items. this algorithm can compute simultaneously frequent and frequent closed itemsets. it lays a foundation for efficiently mining irredundant rules

    本文分析了目前已有的部分關聯規則挖掘演算法，並基於aprioritid演算法和挖掘閉包項集演算法close提出了一種新演算法ffc演算法，這個演算法在挖掘出頻繁項集的同時還得出了閉包頻繁項集，有效地為挖掘無冗餘規則奠定了基礎。
  7. Paper presentes a new algorithm of mining frequent item sets with negative items

    論文提出一種新的挖掘含負項目的頻繁項集演算法，即基於頻繁模式樹的演算法。
  8. 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。
  9. 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演算法的固有缺陷，對不產生候選挖掘頻繁項集方法- - fp - growth頻集演算法進行分析並加以實現。
  10. According to this novel algorithm, the set of frequent items can be derived from the idea of k - weight - estimate, and next, association rules can be discovery according to the matrix - weighted confidence

    該演算法首先根據k -權值估計思想找出頻繁項集，然後根據矩陣加權置信度找出關聯規則。
  11. Considering the defects of typical algorithm for mining frequent itemsets, this dissertation puts forward hy algorithm which is designed to mine association rules and based on the hash technique and the optimized transaction reduction technique

    針對經典頻繁項集挖掘演算法的不足，提出了進行關聯規則數據挖掘的基於散列技術和優化的事務壓縮技術的hy演算法。
  12. The paper propose a new means to mine multidimensional association rules based on multidimensional frequent items set by two steps. firstly we obtain inter - dimension association rules by combining data cube technique with apriori method efficiently

    本文中對基於多維的頻繁項集的演算法進行了探索和演算法優化，尤其是通過採用了維搜索和散列的技術方法而使得系統的挖掘性能大大提高。
  13. The algorithm is based on the frequent pattern tree, which uses for reference a compressed storage data structure i. e. frequent pattern tree of fp _ growth algorithm. it mines frequent item sets with negative items through extending frequent patterns on the tree

    該演算法借用fp _ growth演算法中頻繁模式樹這種壓縮存儲事務的數據結構，通過頻繁模式樹進行模式擴展，挖掘含負項目的頻繁項集。
  14. 3. in the paper we research the apriori arithmetic of association rules, probe into several efficient methods to improve the apriori arithmetic, and introduce emphatically a mining method without generating candidate frequent itemsets : fp - tree

    3研究關聯規則apriori演算法，分析了傳統的關聯規則理論基礎、經典演算法，探討了提高apriori演算法效率的幾種方法，著重介紹一種不產生候選挖掘頻繁項集的方法。
  15. In the paper, the means is researched to mine multidimensional association rules and a effective means based on multidimensional frequent items set by practice in students information system is found

    本文通過在學生信息管理系統中的具體實踐和運用，對多維關聯規則數據挖掘技術進行了探索，實現了基於多維頻繁項集進行多維關聯規則數據挖掘的一種實用高效的方法，並建立了一個高效的學生信息關聯規則挖掘系統。
  16. 2. to the problem that the data table will be searched many times in mining of associative rules, an algorithm using with equivalence classes concept of rough analysis in the mining of single - dimensional boolean associative rules is introduced. the algorithm uses multiple minimal support thresholds instead of single minimal support threshold to settle with its limitation in the finding of frequent items, which makes the resultant rules set more proficiency, and including more significant rules

    針對關聯規則挖掘過程中多次搜索數據表的問題，將rough分析的等效類概念引入到關聯規則挖掘中，針對單維布爾關聯規則問題提出一種挖掘演算法，同時針對單一的最小支持度閾值的缺點，提出使用多個最小支持度閾值來進行頻繁項集挖掘，可使得結果規則集合更加精練，包含更多的有意義規則。
  17. Based on the confidence property and interest threshold, this paper uses for reference apriori algorithm and makes a method for extracting out generalizing associaton rules with negative items from the frequent itemsets with negative items

    論文基於置信度性質和興趣度閾值，並借用apriori演算法，從挖掘出的含負項目的頻繁項集中提取出含負項目的一般化關聯規則。
  18. Not only the existing algorithms of mining negative association rules and association rules with negative items are very few, but also they are essentially based upon the iterative algorithms of apriori idea, which needs multiple times scanning data sets and generating large amounts of frequent candidate sets

    現有的挖掘負關聯規則以及含負項目的關聯規則演算法為數不多，而且本質上都是基於apriori思想的迭代演算法，需要對數據集進行多次掃描，同時生成大量的候選頻繁項集。
  19. 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，並針對apriori演算法的固有缺陷，對不產生候選挖掘頻繁項集方法- - fp - growth頻集演算法進行分析並加以實現。
  20. A new data structure frequent tree is constructed, which stores crucial information of frequent item. and an algorithm of mining frequent itemsets is presented based on the frequent tree, which can avoid repeated databases scans and a huge candidate itemsets generation, and can dramatically reduce the search space

    第三章構造了一個新的數據結構頻繁樹，用以存儲頻繁項集的重要信息，並給出了基於該頻繁樹的頻繁集的挖掘演算法，該方法能夠避免重復掃描數據庫，避免產生大量的候選集，大大地減少搜索空間。
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