frequent itemset 中文意思是什麼

frequent itemset 解釋
頻繁項集
  • frequent : adj. 1. 屢次的，常見的；頻繁的。2. （脈搏等）急促的，快的。vt. 1. 常去，時常出入于。2. 與…時常交際[來往]。adv. -ly
  1. 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。
  2. 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演算法的時間復雜性影響是線性的；但演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。
  3. It is based on the fact that unique labeled graph can be transformed into the format of itemset, on which recent 10 years research on frequent pattern mining can be applied

    由於唯一標號圖能轉換為項集的形式，這就能充分利用近10年來的研究成果。唯一不同的地方是在連通性上的進一步考慮。
  4. As to solving related regular increments upgrade problem, stored the assembling itemset of the original data set and the new data set in the middle table. then used minimum support to cut on the itemset, found the frequent set

    對于關聯規則的增量更新問題，提出採用中間表中存儲原數據集項集和新數據集項集的集合，然後在此集合上使用最小支持度進行剪枝，找到頻繁集。
  5. 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。
  6. 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。
  7. The main contributions of this paper are as follows : we present an efficient algorithm for mining fuzzy frequent itemsets, called fmf. we use ffp tree structure to store frequent item sets imformation, and store ids of transactions related with fuzzy item in tree nodes. in fmf, we can count a fuzzy itemsets support through finding all trasactions including them. we needn ’ t to scan database all. to generate itemset { a } + x ( i. e

    本文的主要工作如下: （ 1 ）針對模糊頻繁集的挖掘問題，提出了一種有效的fmf演算法，在該演算法中採用ffp -樹結構，將與模糊項目相關的事務的序號保存到樹結點中。計算一個模糊項集的支持度，可以通過直接找到所有包含該項集的全部事務進行計算，而不必掃描整個數據庫。
  8. Super set of iemset x ) according to constrained subtree of itemset x, if item “ a ” isn ’ t a fuzzy item, we don ’ t scan database in addition. it can be generated by ffp tree. we propose two order methods for constructing ffp tree. one is that sorting database attributes holding frequent item in ascending order of their nodes number in ffp tree. another is that sorting frequent item of not fuzzy attributes in descending order of their support firstly, then sorting database fuzzy attributes with frequent item in ascending order of their nodes number in ffp tree. our experimental results show that although fmf needs more space costly than the algorithms based on apriori, its time costly is obviously lower than the latter

    針對ffp -樹的生成，提出了兩種排序方法:按屬性順序將每個屬性下的頻繁項目依次插入到頭表中，屬性按照其在ffp -樹中可能的不同結點的個數從少到多進行排序;先對非模糊屬性下的頻繁項目按支持度從大到小進行排序，再對模糊屬性按其在ffp -樹中包含的不同結點的個數，從少到多進行排序，然後依次將各屬性下的頻繁項目插入到頭表中。
  9. A bit string array - based mining algorithm for maximum frequent itemset

    基於位串數組的最大頻繁項目集挖掘演算法
  10. 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演算法都是採用類似apriori演算法的思想即一個頻繁集的任意子集都是頻繁集。
  11. 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。
  12. 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。
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