apriori 中文意思是什麼
apriori
解釋
當場的-
In sequential pattern, we describe mfr and the algorithm on how to find frequent access paths. in mining association rules, we introduce famous apriori algorithm and propose the optimized dhp algorithm with hashing
在關聯規則的挖掘中，深入的分析了經典的apriori演算法，並運用哈希技術改進它得到dhp演算法，其中詳細闡述了該演算法改進的思路。 -
And then it emphasizes the structure model and the process of data warehouse, the ways of extracting data from operation model and guiding into the data warehouse, the realization techniques of dealing with data synthetically, as well as the mechanism of how to superadd the upper data into the data warehouse, and introduces the realization techniques of the mining model of association rule, the apriori and the fp - growth arithmetic particularly
接著重點討論了數據倉庫的構建模型和構建過程，從操作型環境抽取數據並導入數據倉庫方法，對數據進行綜合處理的實現技術，以及後期數據如何追加到數據倉庫的機制，並詳細介紹了關聯規則挖掘模型， apriori演算法，和fp - growth演算法的實現技術。 -
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。 -
Based on the fact of generating the synthetic data using poisson distribution function and exponential distribution function, the performance of hy algorithm and the comparison among hy algorithm, apriori algorithm and dhp algorithm is experimented. these experiments include the one that compares the execution time using variant synthetic data and variant minimum supports, and the scale - up one that compares the execution time using variant transaction number and variant item number in synthetic data. finally the results of the experiments are analysed
在構造基於泊松分佈函數和指數分佈函數的合西南交通大學碩士研究生學位論文第iii頁成數據的基礎上，對hy演算法的性能及其與apriori演算法和dhp演算法的比較進行了實驗，這些實驗包括針對不同的合成數據和不同的最小支持度，對各演算法的執行時間進行比較的實驗以及針對合成數據的不同的事務數和不同的項數對各演算法的執行時間進行比較的規模實驗，並對實驗結果進行了分析，反映出hy演算法具有良好的性能。 -
In the study on level wise search algorithm, the structure and character of three basic frequent set discovery algorithms ais, apriori and dhp are studied by simulation. a model of level wise search algorithm is given and its time complexity is analyzed respectively on transaction scale, item length and support parameter
在分層搜索演算法的研究方面，本文模擬分析了ais 、 apriori和dhp三種基本演算法的結構和特點，構造了一個分層搜索演算法的時間復雜度模型，從事務規模、數據項平均長度和支持度三個方面，對分層搜索演算法的時間復雜性進行了分析和驗證。 -
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。 -
The framework of topology is based on apriori with the idea of " isomeromorphism ", using the techniques of graph sequential expression and label - connectivity determination. topology can analyze the complex relations among the objects in th
這是一個以apriori思想為主體，以先同分后異構為框架，以圖的序列化及矩陣表示和標號連通判定等技術為手段的一個綜合演算法。 -
Measurement of association rules correlation association rules generated by apriori algorithm includes some useless and even misleading rules. to gain more effective rules, a statistical criteria, chi - squared was used to measure the associations, furthermore the quantitative relations between the chi -.
為了使生成的規則更有效，引入了統計學中的卡方檢驗從統計意義上檢驗規則是否關聯，並找到卡方檢驗值與相關系數的數量關系，實現了兩種方法的統一，並用基於相關系數的演算法去生成關聯規則。 -
In the data mining prototype system, apriori algorithm of association rules mining, id3 algorithm of decision tree classification, c4. 5 pessimism estimate algorithm of decision tree classification and c4. 5 reduced - error pruning algorithm of decision tree classification are realized
在數據挖掘原型系統中，實現了關聯分析的apriori演算法、分類的id3決策樹演算法、 c4 . 5的悲觀估計決策樹演算法和c4 . 5決策樹的消除誤差修剪演算法（ reduced - errorpruning ） 。 -
Experiments show that fpmine runs two times as fast as the most recently proposed algorithm fp - growth and saves half the memory ; moreover, our algorithm has a quite good time and space scalability with the number of transactions, and has an excellent performance in dense database mining as well ; spf has excellent time and space efficiency ; fpmine - spf algorithm has a far taster speed in association rules mining than the widely used apriori algorithm and has wonderful scalability. association rules mining always generates too many rules, which makes ii difficult to pick the valuable rules where from
實驗表明，在相同數據集上，與fp - growth演算法相比，演算法fpmine的挖掘速度提高了一倍以上，而所需的存儲空間減少了一半；隨著數據庫規模的增大，演算法fpmine具有很好的可伸縮性；對于稠密數據集，本文演算法也具有良好的性能； spf演算法具有極好的時空效率； fpmine - spf演算法挖掘關聯規則的速度遠快于較長期以來廣泛使用的apriori演算法，並有相當好的可伸縮性。 -
By the way this paper chooses the more effective dm algorithms by deep study in quite a few known algorithms employed in association rules, frequent episodes rules and trend analysis. to speed up producing association rule, this paper also introduces the aadd algorithm which is a upgrade algorithm from apriori
同時本文通過對關聯規則、模式序列和趨勢分析的多種演算法的深入的分析和比較，結合網路攻擊檢測的需求，為檢測dos攻擊選擇合適有效的演算法；並為加快關聯規則挖掘速度，提出apriori演算法的改進演算法? ? aadd演算法。 -
Bss problems are to separate or extract individual source signal from a set of mixture signals. except that the source signals are assumed to be independent, no apriori information is known about the mixture signals
Bss問題是從某類混合信號序列中分離或估計各個未知源信號的過程，其中假設源信號是相互統計獨立的。 -
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。 -
While most of these algorithms are based on apriori line, will generate a huge number of candidate itemsets, need multiple scans over database, and maintain a big hash tree, so the time and space complexity is too high
這些演算法大多基於apriori演算法，在挖掘頻繁模式時需要產生大量候選項集，多次掃描數據庫和維護一棵很大的hash樹，時空復雜度過高。 -
Specifically, aiming at two widely used algorithms in data mining, naive bayesian classifier and boolean association apriori algorithm. we have brought forward two corresponding protocols incorporating privacy concerns. we have used secure multi - party computation protocols and tools to get the solutions
本文針對數據挖掘中應用較為廣泛的樸素貝葉斯分類器和關聯規則的apriori演算法，利用安全多方計算的理論和工具，給出了與其相應的隱私性演算法。 -
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。 -
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。 -
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。 -
It shows obviously that there are two extreme trends about economics methods used at present : one is empirical normal study, the other is apriori doctrine study
分析表明目前的農業經濟學研究中存在著兩種明顯的極端傾向：一種是經驗式的規范研究；另一種是先驗的理論教條傾向。 -
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。
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