問題歸約圖 的英文怎麼說

中文拼音 [wèntíguīyāotú]
問題歸約圖 英文
problem reduction graph
  • 問 : Ⅰ動詞1 （請人解答） ask; inquire 2 （詢問; 慰問） question; ask about [after]; inquire about [aft...
  • 題 : Ⅰ名詞1. （題目） subject; title; topic; problem 2. （姓氏） a surname Ⅱ動詞（寫上） inscribe; write
  • 歸 : Ⅰ動詞1 （返回） return; go back to 2 （還給; 歸還） return sth to; give back to 3 （趨向或集中於...
  • 約 : 約動詞[口語] （用秤稱） weigh
  • 圖 : Ⅰ名詞1 （繪畫表現出的形象; 圖畫） picture; chart; drawing; map 2 （計劃） plan; scheme; attempt 3...
  • 問題 : 1 （需回答的題目） question; problem 2 （需研究解決的矛盾等） problem; matter 3 （事故或意外） tr...
  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. As an example, the parallel machine scheduling problem is mapped on a non - constrained matrix construction graph, and a aco algorithm is proposed to solve the parallel machine scheduling problem. comparison with other best - performing algorithm, the algorithm we proposed is very effective. the finite deterministic markov decision process corresponding to the solution construction procedure of aco algorithm is illustrated in the terminology of reinforcement learning ( rl ) theory

    本章最後提出了解決并行機調度問題的蟻群演算法，該演算法把并行機調度問題映射為無約束矩陣解構造圖，並在演算法的信息素更新過程中應用了無約束矩陣解構造圖的局部歸一化螞蟻種子信息素更新規則，與其他幾個高性能演算法的模擬對比試驗證明這種方法是非常有效的。
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