algorithm data attributes 中文意思是什麼

algorithm data attributes 解釋
算術數據屬性
  • algorithm : n. 【數學】演算法；規則系統；演段。
  • data : n 1 資料，材料〈此詞系 datum 的復數。但 datum 罕用，一般即以 data 作為集合詞，在口語中往往用單數...
  • attributes : 屬性特徵
  1. Optimized association rules are permitted to contain uninstantiated attributes. the optimization procedure is to determine the instantiations such that some measures of the roles are maximized. this paper tries to maximize interest to find more interesting rules. on the other hand, the approach permits the optimized association rule to contain uninstantiated numeric attributes in both the antecedence and the consequence. a naive algorithm of finding such optimized rules can be got by a straightforward extension of the algorithm for only one numeric attribute. unfortunately, that results in a poor performance. a heuristic algorithm that finds the approximate optimal rules is proposed to improve the performance. the experiments with the synthetic data sets show the advantages of interest over confidence on finding interesting rules with two attributes. the experiments with real data set show the approximate linear scalability and good accuracy of the algorithm

    優化關聯規則允許在規則中包含未初始化的屬性.優化過程就是確定對這些屬性進行初始化，使得某些度量最大化.最大化興趣度因子用來發現更加有趣的規則;另一方面，允許優化規則在前提和結果中各包含一個未初始化的數值屬性.對那些處理一個數值屬性的演算法進行直接的擴展，可以得到一個發現這種優化規則的簡單演算法.然而這種方法的性能很差，因此，為了改善性能，提出一種啟發式方法，它發現的是近似最優的規則.在人造數據集上的實驗結果表明，當優化規則包含兩個數值屬性時，優化興趣度因子得到的規則比優化可信度得到的規則更有趣.在真實數據集上的實驗結果表明，該演算法具有近似線性的可擴展性和較好的精度
  2. The discussion of main parallel technologies on construction of parallel sliq algorithm is presented in this paper. the computing result of algorithm complexity of sequential and parallel algorithm indicates : when the data set is large enough, as to continuous attributes, the parallel algorithm almost get speedup value equal to the number of processors ， while as to categorical attribute the improvement of parallel algorithm is limited

    通過對串列和并行演算法時間復雜度的計算表明，當數據集充分大時，由於連續屬性的排序計算操作分散到各個處理機單元上進行，顯著降低了計算時間，從而可以得到近似於處理機個數的加速比，對于離散屬性，本并行演算法對串列演算法的性能提高有限
  3. Subsequently, clustering analysis in data mining is disserted, involving the methods and characteristics of clustering used in data mining and the methods for evaluating the clustering results, with emphasis on clustering the data with categorical attributes. k - modes clustering algorithm and its variations are introduced with their advantages and disadvantages

    在此基礎上對數挖掘中的聚類分析作以詳細地論述，總結了數挖掘中聚類分析的方法和特點，並對聚類結果的評價方法進行了討論，重點討論了分類屬性數據聚類，具體研究了k - modes演算法及其變形，並指出了它們的優缺點。
  4. Methods for inducing decision tree in distributed database system are described and a distributed algorithm based on id3 is proposed. using a new data structure called attributes distribution list this algorithm can be scalable and parallelized

    該演算法在傳統的id3演算法的基礎上引進了新的數據結構：屬性按類別分佈表，使得演算法具有可伸縮性和并行性。
  5. 3 ) semantic classification model based som network we use the classification model to combines attributes within a database. this is done using an unsupervised learning algorithm. the output is used as training data for the next stage

    3 ）基於som網路的語義分類模型設計建立som網路模型，將元數據特徵向量進行分類，形成bp網路的目標向量，用於匹配規則的提取。
  6. Using the statistic characterization of data, the relevant knowledge reduction algorithm is put forward by combining the probability with classification rules ; using the characterization of fuzzy attributes, the decision system with subjection degree attribute is built by combing the rough set theory and fuzzy set theory, and the idea of distinguish matrix is induced to the concealed decision system to reduce data

    利用數據的統計特徵，將概率測度與分類規則結合起來，提出了相應的知識西北工業大學博士學位論文約減演算法;利用模糊屬性集合的特點，把粗糙集合與模糊集合有機結合起來，將粗糙集中分辨矩陣的思想引入到具有隸屬度屬性的隱式決策系統中進行數據約減。
  7. Firstly, influence factors of generalization of neural network are presented in this thesis, in order to improve neural network ’ s generalization ability and dynamic knowledge acquirement adaptive ability, a structure auto - adaptive neural network new model based on genetic algorithm is proposed to optimize structure parameter of nn including hidden layer nodes, training epochs, initial weights, and so on ; secondly, through establishing integrating neural network and introducing data fusion technique, the integrality and precision of acquired knowledge is greatly improved. then aiming at the incompleteness and uncertainty problem consisting in the process of knowledge acquirement, knowledge acquirement method based on rough sets is explored to fulfill the rule extraction for intelligent diagnosis expert system, by completing missing value data and eliminating unnecessary attributes, discretization of continuous attribute, reducing redundancy, extracting rules in this thesis. finally, rough sets theory and neural network are combined to form rnn ( rough neural network ) model for acquiring knowledge, in which rough sets theory is employed to carry out some preprocessing and neural network is acted as one role of dynamic knowledge acquirement, and rnn can improve the speed and quality of knowledge acquirement greatly

    本文首先討論了影響神經網路的泛化能力的因素，提出了一種新的結構自適應神經網路學習演算法，在新方法中，採用了遺傳演算法對神經網路的結構參數（隱層節點數、訓練精度、初始權值）進行優化，大大提高了神經網路的泛化能力和知識動態獲取自適應能力;其次，構造集成神經網路，引入數據融合演算法，實現了基於集成神經網路的融合診斷，有效地提高了知識獲取的全面性、完善性及精度;然後，針對知識獲取過程中所存在的不確定性、不完備性等問題，探討了運用粗糙集理論的知識獲取方法，通過缺損數據補齊、連續數據的離散、沖突消除、冗餘信息約簡、知識規則抽取等一系列的演算法實現了智能診斷的知識規則獲取;最後，將粗糙集理論與神經網路相結合，研究了粗糙集-神經網路的知識獲取方法。
  8. Followed by the rapid extension of data size, the usage of parallel technology is a very important method to improve the efficiency of data ming. sliq uses novel pre - sorting and breadth - first techniques to build a decision tree fast and accurately on a large data set, and can deal both categorical and numeric attributes. but the primary algorithm contains the abundant computing on attribute and record

    本文首先分析了串列sliq演算法的原理和特點，針對其不足提出了一些改進方法，然後在基於pvm的環境下實現了演算法的并行化，分析了演算法的時間復雜度和加速比，提高了sliq演算法的效率，具有一定的理論意義和實用價值。
  9. Firstly, this paper improves single dimensional association rule mining algorithm aprioritidlist based on deep research on association rule mining algorithms, and advances an efficient multidimensional association rule mining algorithm aprioritidlist + that is suitable for vulnerability database of rdbms. furthermore, the algorithm is applied on vulnerability database including data preparation, implement of the algorithm and analysis of experiment results, where data preparation is mainly to select some from numerous vulnerabilities and vulnerability attributes that are suitable for association rule mining to do experiments, meanwhile do the discrete process on quantified attribute values

    本文首先在深入研究關聯規則挖掘演算法的基礎上，對其中的單維關聯規則挖掘演算法aprioritidlist進行改進，提出了一種適合關系型弱點數據庫的高效的多維關聯規則挖掘演算法aprioritidlist + ;並且將該演算法應用到弱點數據庫中，包括數據準備、演算法實現和實驗結果的分析，其中數據準備主要是對數量龐大的弱點信息和弱點屬性進行挑選，取出一部分適合於關聯規則挖掘的弱點信息來進行實驗，同時也對量化屬性值進行了離散化處理。
  10. We analyze the previous replication algorithms, and bring forward a new replication algorithm on the basis of ‘ access rate ’. different from the previous which account the access of data items, the algorithm records the relative access rate of attributes. in the way, a large amount of memory space and cpu time is saved

    文章在分析以往數據復制緩存演算法優缺點的基礎上，提出了基於訪問率的數據復制緩存演算法，將對于每個數據項的使用評估轉換為對于屬性的評估，能夠減輕系統的存儲和計算負擔。
  11. The dissertation suggests a series of algorithms to compute the sets and numeric data based on the vp - md model. to obtain the minimal reduction of attributes and the minimal reduction of values, the dissertation provides a csbark algorithm based on the context sensitivity ( cs ) of attributes and a minimal rules set algorithm based on value core

    本文研究了基於vp - md模型計算粗糙集理論所涉及的數字量和集合量方法，並針對最小屬性約簡和最小值約簡這兩個np問題提出了基於屬性上下文敏感度的啟發式屬性約簡演算法? csbark演算法和基於值核的最小規則集求解演算法。
  12. On the base of extend rough set theory, this paper put forward the improved data mining algorithm with priority attributes based on the priority relation. therefore, the classification precision of basic rough set and rough set with priority attributes reached unification and the classification rules by this model in the section are more curtail and rational. 4

    論文在粗糙集拓廣理論的基礎上，利用屬性的有序特性即優先二元關系，提出有序屬性的數據挖掘改進演算法，使基本粗糙集和帶有準則的粗糙集在挖掘分類精度上達到統一，且挖掘出的規則簡練、更具合理性和綜合性。
  13. This paper first illustrated some typical algorithms for large dataset, then gave off a processing diagram in common use second, for the dataset with large quantity and many attributes, we renovated the calculation method of the attribute ' s statistic information, giving off a ameliorated algorithm this thesis consists of five sections chapter one depicts the background knowledge and illustrates the position of data mining among many concepts also here is the data mining ' s category chapter two describes the thought of classification data mining technique, puts forward the construction and pruning algorithms of decision tree classifier chapter three discusses the problems of adapting data mining technique with large scale dataset, and demonstrates some feasible process stepso also here we touches upon the combination r - dbms data warehouse chapter four is the design of the program and some result chapter five gives the annotation the conclusion, and the arrangement of future research

    本論文的組織結構為：第一章為引言，作背景知識介紹，摘要闡述了數據挖掘在企業知識管理、泱策支持中的定位，以及數據挖掘的結構、分類；第二章講述了分類數據挖掘的思路，重點講解了泱策樹分類器的構建、修剪，第三章針對大規模數據對數據挖掘技術的影響做了講解，提出了可採取的相應的處理手段，以及與關系數據庫、數據倉庫結合的問題；第四章給出了論文程序的框架、流程設計，以及幾個關鍵問題的設計；第五章對提出的設計進行簡要的評述，做論文總結，並對進一步的研究進行了規劃。
  14. We introduce data mining technology in data analyzing, and extend algorithms of this system based on exploiting algorithms in data mining, such as conjunction analyzing algorithm and serial mode analyzing algorithm, which can extract security related attributes of system characteristic efficiently, promote the scalability of the system greatly and provide data support for insight research toward the system

    在數據分析中引入了數據挖掘技術，在利用數據挖掘中的關聯分析、序列模式分析等演算法的基礎上，針對本系統對演算法進行了擴展，能夠有效的提取與安全相關的系統特徵屬性，大大提高了系統的可擴展性，並且對系統的深入調查提供數據的支持。
  15. This algorithm views data in page as a complex object that contains attributes and has a hierarchical structure

    該演算法視頁面中的數據為包含屬性且具有層次結構的復雜對象。
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