數據分類演算法 的英文怎麼說

中文拼音 [shǔjùfēnlèiyǎnsuànfǎ]
數據分類演算法 英文
data-sorting algorithm
  • 數 : 數副詞（屢次） frequently; repeatedly
  • 據 : 據Ⅰ動詞1 （占據） occupy; seize 2 （憑借; 依靠） rely on; depend on Ⅱ介詞（按照; 依據） according...
  • 分 : 分Ⅰ名詞1. （成分） component 2. （職責和權利的限度） what is within one's duty or rights Ⅱ同 「份」Ⅲ動詞[書面語] （料想） judge
  • 類 : Ⅰ名1 （許多相似或相同的事物的綜合; 種類） class; category; kind; type 2 （姓氏） a surname Ⅱ動詞...
  • 演 : 動詞1 （演變; 演化） develop; evolve 2 （發揮） deduce; elaborate 3 （依照程式練習或計算） drill;...
  • 算 : Ⅰ動詞1 （計算數目） calculate; reckon; compute; figure 2 （計算進去） include; count 3 （謀劃;計...
  • 法 : Ⅰ名詞1 （由國家制定或認可的行為規則的總稱） law 2 （方法; 方式） way; method; mode; means 3 （標...
  • 數據 : data; record; information
  1. This article canvass the status quo of the archive ' s automatization administration and the develop status of data mining, and discusses how to combine the data mining technology with the archive work from data cleaning means, data mining arithmetic, and data storage etc. and this article put forword a data mining syst em design idea. this article ' s structure is : first, in allusion to the archive data status quo, the pretreatment work of archive data that include data quality evaluation, data cleaning and data commut - ation process is bringed forword ; second, in the process of realizating data mining, the article discusses conception description, association rule, class three familiar means of applicating data mining, also put inforword the concrete arithmetic and the program design chart, and discusses the range and the foreground of all kinds of arithmetic when they are applicated in the archive ; third, the base of so you say, this article also discusses the importance of the archice applicate data storage and the means of realizing it ; last, the article discusses seval important problem of realizing an archive data mining system from data, diversity, arithmetic multiformity, mining result variety and the data pretreatment visibility, mining object descriptive visibility, mining process visibility, mining result visibil ity, user demand description and problem defining etc aspect. the article ' s core is how to import data mining technology in the archive work

    本文評述了檔案自動化管理現狀和數據挖掘技術的發展狀況，從數據清洗方法、數據挖掘演算法、數據倉庫的建立等方面論述了如何將數據挖掘技術與檔案工作相結合的具體思路，並提出了一個數據挖掘系統的設計思想。文章首先，針對檔案數據的現狀，提出了應對檔案數據進行預處理工作，包括數據質量評估、數據清理、數據變換和歸約等過程；其次，在具體實現數據挖掘過程中，本文結合檔案數據的特點探討了概念描述、關聯規則、分類等三種常見挖掘形式的實現方法，提出了具體的實現演算法和程序設計框圖，並論述了各種演算法在檔案工作中的應用范圍及前景；第三，在上述基礎上，又論述數據倉庫在檔案數據挖掘中的重要性並提出了實現一個檔案數據倉庫的方法；最後，從處理數據的多樣性、演算法的多樣性、挖掘結果的多樣性、數據預處理可視化、挖掘對象描述的可視化、挖掘過程可視化、結果顯示可視化、用戶需求的描述及問題定義等幾方面討論了實現一個檔案數據挖掘系統的幾個重點問題。全文以探討如何將數據挖掘技術引入到具體的檔案工作實踐中為核心。
  2. In the implementation of data classifier, we describe extraction and management of conceptual hierarchy for data, also design an automatic extraction algorithm for numeric data. in this section, we still provide the two algorithms of concept - based attribute - oriented induction and evaluating classification scheme and the visualization of classification rule. finally, the data classifier is tested in databas the results show that it is practical and its performance meet the requirement of designing

    然後，在數據分類器的實現中，論述了數據的概念層次提取和管理，並對數值型數據給出了一個自動提取概念層次演算法；同時給出了基於面向屬性歸納的分類演算法、分類模式的評價演算法和分類規則的可視化方法。
  3. In this paper, a lot of researches and exploration are applied to studying the universality and expansibility of hardware and the arithmetic design and code optimization of software. especially, all of the following arithmetics or conceptions are worked out in the research of software design : self - adaptable compression arithmetic based on dictionary model for data collection system, similarity full binary sort tree, a optimized quick search arithmetic and an improved arithmetic of multiplication in the floating - point operation. and all of the arithmetic are designed with mcs - 51 assembly language. the quick search arithmetic, in which merits of both binary search and sequence search are used fully, are based on the specialty of preorder traversal in similarity full binary sort tree

    特別在軟體設計研究中，提出了適用於數據採集系統的數據壓縮演算法? ?基於字典模型的自適應壓縮演算法；提出了類滿二叉排序樹的定義；提出了基於類滿二叉排序樹的先序遍歷特性的最優化快速查找演算法，它充分利用了折半查找和順序查找各自的優點；提出了浮點運算乘法的改進演算法；並在mcs - 51匯編語言層次上對所有的演算法加以實現。
  4. This thesis expatiates on the state - of - the - art of dm technique, with emphasis on data mining algorithms such as clustering analysis, classification analysis, dependence analysis and statistical analysis. a comparative study of three popular dm tools ( ibm intelligent miner, spss clementine and sas enterprise miner ) is carried out. the future trends of dm technology are also revealed

    論文闡述了數據挖掘技術在國內外的研究現狀，對目前主要的數據挖掘演算法如聚類分析、分類分析、相關分析和統計分析進行了剖析，對當前最為流行的數據挖掘工具ibmintelligentminer 、 spssclementine及sasenterpriseminer進行比較分析，闡述了數據挖掘技術的未來發展趨勢。
  5. It is recommended to choose customer subdivision algorithm according to data character and user intention. lastly an algorithm of measuring interestingness of data mining schema based on user expectation and fuzzy logic is presented

    文章對部分應用於crm的數據挖掘演算法進行了深入研究，提出根據數據特徵和細分目的選擇演算法，並給出了用於不同標準的客戶細分聚類演算法。
  6. In the second chapter, the thesis introduces some primary concepts of data mining, investigates and discusses the key technology of data mining, including outlier analysis, clustering and classification. and the main algorithms of data mining, which the later chapters of this thesis refer to, are also given in detail

    第二章介紹了數據挖掘的一些基本概念，對數據挖掘的主要技術- - - -離群分析、聚類以及分類技術作了深入的研究和探討，並在此基礎上詳細地給出了本文後繼章節中所涉及到的主要數據挖掘演算法。
  7. This tutorial walks you through scenarios for targeted mailing, forecasting, market basket analysis, and sequence clustering, to demonstrate how to use the data mining algorithms, mining model viewers, and data mining tools that are included in microsoft sql server 2005 analysis services ssas

    本教程將指導您演練目標郵件、預測、購物籃分析以及順序分析和聚類分析等方案，闡釋如何使用microsoft sql server 2005 analysis services （ ssas ）提供的數據挖掘演算法、挖掘模型查看器以及數據挖掘工具。
  8. The algorithm firstly calculates the nonlinear principal poly line go through the middle of trained samples of hyperspectral image, and then classifies every pixel by projecting the spectral vector of the pixel on the principal poly line

    建立了一種基於非線性主折線的分類演算法，該方法將利用訓練樣本數據，構造非線性主折線，通過判斷原始數據在主折線上的投影對像元進行分類。
  9. Bayesian classification is based on bayesian theorem. it can be comparable in interpretability with decision tree and in speed with neural network classifiers. bayesian classifiers have also exhibited high accuracy and speed when applied to large databases

    該演算法基於貝葉斯定理，可解釋性方面可以與判定樹相比，準確度可和神經網路分類演算法相媲美，用於大型數據庫時該演算法已表現出高準確度與高速度。
  10. In this paper the algorithm of the data classification in the data mining is regarded as the main research target

    本文以數據挖掘中的數據分類演算法為主要研究對象。
  11. The algorithms of text classification are supervised, which means the classifier training need some human labeled data of fixed classes. generally, the accuracy of classifier is higher with more labeled data. but the labeled data by hand are expensive resource

    文本分類演算法是有監督的學習演算法，它需要一個分類好的，類別已標識的文本數據集訓練分類器，然後用訓練好的分類器對未標識類別的文本分類。
  12. At the realization of the system, we analyze the integral structure and working principle of our system at first. then, we show the relationship among tables in core database. lastly, we study automatic document categorization algorithm and propose algorithm descriptions and experiment results of chinese language segmentation, schema matching of paper titles and clustering

    在系統實現部分分析了系統的整體結構和工作原理，介紹了系統核心數據庫中各表的聯系，最後重點研究了文檔自動分類演算法，給出了漢語分詞演算法、論文標題模式匹配演算法、聚類演算法的演算法描述及實驗結果。
  13. Some classical clustering algorithms and decision trees algorithms are analyzed and compared

    並具體分析比較了多種的典型聚類和決策樹數據挖掘演算法。
  14. This dissertation, in the light of the limitations of existed methods, suggest an algorithm based on conception hierarchy tree for data mining, constructing tree from bottom to top through the method of variedly dividing interval and realizing conception hierarchy construction and conception exaltation isochronously

    本文針對已有數據分類演算法的不足，採用「變間隔分割初始區間」和「概念層次構建與概念提升同步」的方式「自底向上」地構建概念層次樹，提出並實現了基於概念層次樹的數據挖掘改進演算法。
  15. To solve these problems, this thesis proposed a new model for the intrusion detection system that based on the data mining. we have discussed some key technical problems and related solutions. we apply some existing algorithms of association analysis, sequence pattern analysis, and data classification to the intrusion detection system

    針對這些問題，本文採用了一種基於數據挖掘技術建立入侵檢測系統的方法，討論了該系統實現中的關鍵技術及解決方法，將現有的數據挖掘演算法中的關聯分析、序列模式分析、分類等演算法應用於入侵檢測系統，對入侵行為提取特徵、建立規則，通過對審計數據的處理與這些特徵進行匹配，檢測入侵，以形成智能化的入侵檢測系統。
  16. Data mining is defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data or known as knowledge - discovery in databases ( kdd ). to do this, data mining uses computational techniques from statistics, machine learning and pattern recognition such as discriminate analysis, regression method, mathematical programming, decision tree, k - nearest neighbor, artificial neural network etc. although many positive attempts are done, the development and application of personal credit assessment model in chinese bank industry is still in its infancy

    數據挖掘是20世紀90年代後期人工智慧和數據庫領域興起的一種數據處理和知識發現（ kdd ）理論，是從大量的、不完全的、有噪聲的、模糊的和隨機的實際應用數據中，提取隱含在其中的信息和知識的過程。對數據進行分類和預測是數據挖掘的主要功能。數據挖掘用於信用評估的優勢主要在於: （ 1 ）能處理和修正實際數據問題，演算法模型具有自檢
  17. Second, a full automatic classification of raw images into textured and non - textured images is presented for large - scale image databases. the algorithm uses region segmentation and statistical testing

    其次針對圖像檢索數據量龐大的特點，按照由簡單到復雜的層次檢索的思想，提出了對圖像進行完全自動的粗分類演算法。
  18. We also make plenty of classification experiments with data sets from various of different fields, and then analyse and compare the classification capacity of several decision tree classification algorithms and the adaptability to different datas

    在來自不同領域的數據集上進行了大量的分類實驗，分析和比較了多種決策樹分類演算法的分類性能和對不同數據的適應性。
  19. The algorithm of sf _ dt, which bases on the idea of decision tree classification algorithm ids, use the means of file splitting take the place of the means which bases on memory. it improves the scalability of classification algorithm and can deal with very large database

    Sf _ dt演算法以決策樹分類演算法id3的基本思想為基礎，用基於文件分割的方法代替原有的基於內存的演算法，提高了演算法的可規模性，可以處理超大規模的數據。
  20. Through analyzing the characteristics of nominal data, clustering algorithm of nominal data based on entropy and hierarchical method was proposed

    摘要通過對標稱數據的分析，提出了一種基於信息熵和層次聚類思想的標稱數據聚類演算法。
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