apriori method 中文意思是什麼

apriori method 解釋
先天方法
  • apriori : 當場的
  • method : n 1 方法，方式；順序。2 （思想、言談上的）條理，規律，秩序。3 【生物學】分類法。4 〈M 〉【戲劇】...
  1. In order to apply the method of negative associate analysis this method has the as its interest measurement and modify the classic method : apriori method

    該方法採用卡方統計量作為興趣度度量，並修改經典關聯分析方法：方法，以進行否定關聯分析。
  2. 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。
  3. The apriori algorithm is the method of finding boolean association rules, but has the disadvantage in the complexity of space and time

    Apriori演算法是挖掘布爾關聯規則的演算法，而該演算法在空間和時間的復雜性有著難以克服的局限性。
  4. 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。
  5. 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。
  6. The paper is about how to analyze web server ' s log file and the technology used and tries to improve it in the following aspects : ( l ) this paper presents a simple processing model on mining web log based on xml storage and the corresponding solutions used to clean and transform log data. ( 2 ) according to the advantages of xml and the self - structure feature of log data, the paper proposes the novel idea that stores log data in xml form, furthermore it discusses the method and implementation on how to store xml - compliant log data into the database by the medium - grained storage means. ( 3 ) this paper addresses an improved algorithm called ufapa for mining user frequent access path on the basis of the algorithm apriori

    本文研究了web日誌挖掘中的相關技術，在以下幾方面進行了改進： （ 1 ）在web日誌挖掘模型的基礎上，對web日誌數據的清洗和轉換提出了相應的解決方法； （ 2 ）結合xml的優勢和web日誌數據的半結構化特點，提出用xml存儲日誌數據並探討了xml形式的日誌數據如何以中粒度方法實現在數據庫中存儲的方案； （ 3 ）結合用戶訪問路徑的特點以apriori演算法為基礎提出了一種改進的挖掘頻繁訪問路徑的ufapa演算法，介紹了演算法思想及演算法描述。
  7. Thirdly, this paper gives out a method of discovering markov blanket of multi - sets rather singleton, and proves the correctness of the method, afterwards this paper expresses it using bayes network too. finally, aimed at the application of educational evaluation, this paper performs post - processing at those rules mined by apriori algorithm, and filters those rules with the constraints of conditional independences, and reads out conditional independences from rules

    面向教育評估系統中的具體應用，本文提出了對原有系統中採用的apriori演算法挖掘的關聯規則進行后處理的方法：採用條件獨立性和傳統支持度- - -可信度框架相結合的方法進行關聯規則的過濾，並從中發現存在的條件獨立性限制。
  8. 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。
  9. One is the maximal forward references ( mf ) method, and it is like the traditional data mining methods, apriori

    一種是採用最大向前路徑（ mf ）方法，最後的步驟類似於傳統數據挖掘中的apriori演算法。
  10. 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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