聚類搜索 的英文怎麼說

中文拼音 [jùlèisōusuǒ]
聚類搜索 英文
cluster seeking
  • 聚 : 動詞（聚集; 聚積） assemble; gather; get together
  • 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...
  • 搜 : 動詞1. （尋找） collect; gather2. （搜查） search; ransack
  • 索 : Ⅰ名詞1 （大繩子; 大鏈子） a large rope 2 （姓氏） a surname Ⅱ動詞1 （搜尋; 尋找） search 2 （要; ...
  • 搜索 : 1 （仔細尋找） search for; ferret about; hunt for; scout around 2 [電子學] hunting; scan; [控] in...
  1. The experimental results show that the method can not only detect the fuzzy edge and exiguous edge correctly, but also improve the searching efficiency of fuzzy clustering algorithm based on tea evidently

    實驗結果表明，該演算法不僅具有很強的模糊邊緣和微細邊緣檢測能力，而且可以提高基於人工免疫進化演算法的模糊聚類演算法的搜索效率。
  2. First, we have expatiated the working principle, performance parameters and major technologies. farther, we have analyzed the shortcomings of the existing catalog search engine and introduced the clustering analysis and the ant algorithm ; on the basis of this, we discussed the possibility and necessity of the connection between them, which avoids the local optimization of the clustering analysis to a degree. in the end, we appraise the idea that we deal with the information data by the data structure of the binary tree, m - branch tree and tree established by the ant algorithm, which can improve the efficiency of the search engine

    首先闡述了搜索引擎的工作原理，性能指標，主要技術；分析了現有目錄式搜索引擎的缺點，接著介紹了聚類分析演算法與螞蟻演算法的理論，並論述了二者結合的可能性和必要性，這種結合方法也在一定程度上克服了聚類分析演算法容易陷入局部最優的缺點，最終提出了通過使用螞蟻演算法建立二叉樹、 m叉樹和樹作為信息數據處理的思想，大大提高了搜索引擎搜索的效率。
  3. In this text, we first do some research on the genetic algorithm about clustering, discuss about the way of coding and the construction of fitness function, analyze the influence that different genetic manipulation do to the effect of cluster algorithm. then analyze and research on the way that select the initial value in the k - means algorithm, we propose a mix clustering algorithm to improve the k - means algorithm by using genetic algorithm. first we use k - learning genetic algorithm to identify the number of the clusters, then use the clustering result of the genetic clustering algorithm as the initial cluster center of k - means clustering. these two steps are finished based on small database which equably sampling from the whole database, now we have known the number of the clusters and initial cluster center, finally we use k - means algorithm to finish the clustering on the whole database. because genetic algorithm search for the best solution by simulating the process of evolution, the most distinct trait of the algorithm is connotative parallelism and the ability to take advantage of the global information, so the algorithm take on strong steadiness, avoid getting into the local

    本文首先對聚類分析的遺傳演算法進行了研究，討論了聚類問題的編碼方式和適應度函數的構造方案與計算方法，分析了不同遺傳操作對聚類演算法的性能和聚類效果的影響意義。然後對k - means演算法中初值的選取方法進行了分析和研究，提出了一種基於遺傳演算法的k - means聚類改進（混合聚類演算法） ，在基於均勻采樣的小樣本集上用k值學習遺傳演算法確定聚類數k ，用遺傳聚類演算法的聚類結果作為k - means聚類的初始聚類中心，最後在已知初始聚類數和初始聚類中心的情況下用k - means演算法對完整數據集進行聚類。由於遺傳演算法是一種通過模擬自然進化過程搜索最優解的方法，其顯著特點是隱含并行性和對全局信息的有效利用的能力，所以新的改進演算法具有較強的穩健性，可避免陷入局部最優，大大提高聚類效果。
  4. After that, the thesis proposes a algorithm to seek the degree of the texture of a digital image. 3rd. it gives a new model for data clustering, " village - town " model and a clustering globally best algorithm based on genetic method

    以待聚類數據集為對象從尋求全局最優配置入手，首次提出了基於遺傳演算法的聚類變精度搜索方案和基於點分佈密度的類合併準則。
  5. Sofm neural networks is embedded into evolutionary strategy ( es ). fitness function is constructed based on the state of sofm neural networks. the sensitivity of sofm neural networks to initial weight matrix and sequence of input exemplars is overcome by the strong global optimum of es

    將sofm網路嵌入到進化策略（ es ）中，根據sofm網路的運行狀態構造es的適應性函數，利用es的強搜索能力，克服sofm網路聚類效果受輸入模式次序和網路初始連接權矩陣的影響。
  6. If it is not good enough, optimal control module based on principle component analysis and clustering search will function for optimization. this algorithm makes no request for accurate analytical model and maintains search efficiency and self - adjusting performance by an optimal parameter set derived from process data, which is proved to be effective. by practical running of dcs, a prominent tracing performance is gained and variation of parameters is limited in a small range, which enhance stability and production benefit of sintering process obviously

    燒結過程是一個影響因素復雜、干擾嚴重、具有不確定性的工業過程，傳統的基於對象精確解析模型的優化策略難以奏效，因此在控制參數優化模塊中提出了一種基於主元分析和聚類搜索的優化匹配演算法，該演算法不要求對象的精確模型，其搜索效率和自校正性能依賴于從生產過程歷史數據中抽取的優化參數樣本庫，充分利用了燒結生產過程積累的生產數據所包含的信息，取得了不錯的效果。
  7. In this paper, we praise the point that it is an efficient way to solve the intelligent search in the search engine by the analysis of the clustering technology and ant algorithm

    本文通過對聚類分析技術、螞蟻演算法的分析，提出了利用知識進化類演算法? ?螞蟻演算法實現搜索引擎中進化搜索的有效方法。
  8. Users of web search engines are often forced to shift through the long ordered list of document " snippets " returned by the engines. this paper applied web content mining to the field of search engine. search engine results clustering relies on the information returned by the search engine

    本文將web內容挖掘技術應用於搜索引擎領域，它依賴于搜索引擎結果所提供的信息來歸納出聚類，使得在搜索引擎返回的非常大的文檔列表中的過濾操作變得十分方便。
  9. Meanwhile it accelerates the search process by cache. ( 3 ) the chinese word segmentation module to support the text segmentation of system, it uses hmm - based disambiguation algorithm to improve the accuracy of the word segmentation. ( 4 ) the search module to response the users ’ search request, it applies an efficient clustering / classification algorithm to optimize the search service quality

    它使用基於hmm模型的歧義消除演算法來提高分詞預處理的切分精度。 （ 4 ）檢索模塊用來響應用戶的查詢請求。它利用簡單靈活的聚類/分類演算法來優化系統的搜索服務。
  10. Conventional clustering criteria - based algorithms is a kind of local search method by using iterative mountain climbing technique to find optimization solution, which has two severe defects - sensitive to initial data and easy as can get into local minimum

    傳統的基於聚類準則的聚類演算法本質上是一種局部搜索演算法，它們採用了一種迭代的爬山技術來尋找最優解，存在著對初始化敏感和容易陷入局部極小的致命缺點。
  11. This paper discusses the application of the characteristic that the fuzzy equivalent relationship equates the normalized distance of the x quotient space in the theoretical model of fuzzy quotient space, and advances the algorithm of text clustering in the fuzzy quotient space

    討論了在模糊商空間理論模型中模糊等價關系與x商空間的歸一化距離等同這一特徵在文本聚類搜索中的應用，並提出了模糊商空間下文本聚類的演算法。
  12. However, their current status is still far from user ' s satisfaction. lt includes : ( 1 ) the content that search engine returns is a enormous flat bill ( information overloading question ) ; ( 2 ) the items return with search engine are not the content that user requisite in deed ( low precision question ) this paper presents a fuzzy ( soft ) clustering algorithm htsc ( hyperlink - text based soft clustering ) using a mixed similarity metric of document content and inter - document hyperlinks, for clustering web search results from a search engine in order to help users find relevant web information more easily

    這主要表現為： （ 1 ）搜索引擎返回的結果是一個龐大的平坦結構的資源清單（即信息負載問題） ； （ 2 ）搜索結果中的信息項並非都是用戶真正需要的信息資源（即低精度問題） ；論文提出了一種基於文檔文本內容和文檔間超鏈信息的混合相似度的模糊（軟）聚類演算法htsc 。該演算法可對搜索引擎返回的結果進行模糊聚類，以方便用戶從中找到真正需要的信息。
  13. After researching deep into the building and combination on model based on database, the author puts forth a search algorithm of data mining by use of prototype library and a data - clustering algorithm on the basis of expanded cf - tree

    本文對基於數據庫的模型生成和模型組合作了較深入的研究，給出了基於原型庫的數據開采搜索演算法和基於擴充cf -樹數據聚類演算法。
  14. This paper proposes a agent - based metasearch engine system in which multiple agents coordinate to attain information with intelligence. in the system, k - modes clustering algorithm is used to produce user ' s personalized information pattern and the personal pattern can be adjusted by user agent automatically. this system adopts metasearch of structure of search engine, regards agent as the systematic basic module, utilizes autonomy and interaction of agent finish individualized internet search of message of user

    本文提出的wiirs系統採用了一種基於agent的集成搜索引擎的結構，以agent作為架構系統的基本組件，利用agent的自治性和協作性來完成用戶的個性化信息搜索，在系統設計中，通過採用agent技術確定用戶檢索的個性化模式，使用k - modes演算法聚類學習用戶反饋信息，產生新的個性化模式，從而可以精確地滿足用戶的個性化檢索需求，降低了檢索結果的重復率，在一定程度上提高了檢索的精度和效率。
  15. The combination of document clustering technique and web search engine has become a hot - spot in document mining area

    文本聚類技術和網路搜索引擎服務相結合，已經成為文本挖掘領域的一個熱點研究課題。
  16. But there are seldom researches in using document clustering technique into chinese web documents and cooperating with chinese web search engine services

    但是，把文本聚類技術應用於中文web文檔，與中文搜索引擎服務相結合的研究仍然比較匱乏。
  17. It tracks user ' s entire searching process, collect information that could be used to determine his related characters and understand his search target better. ( 3 ) through the comparison of different result binding methods of meta - search systems, we recommended the combination of clustering with other techniques. a suitable algorithm for on line meta - search result clustering was also suggested

    （ 3 ）分析了元搜索引擎對查詢代理返回的結果進行處理的基本狀況及方法，提出了採用結果聚類與其它處理方式相結合的方法；並在對幾種有代表性的文檔聚類演算法進行比較后論述了stc演算法在處理元搜索引擎搜索結果在線聚類問題時的優勢。
  18. Some analyses of data feature in urban aerial image are done first, and a split - block - clustering based on fcm method is discussed for the coarse segmentation of building groups, which can reduce the search region for buildings extraction and decrease calculation much more

    首先分析了城市航空影像數據特點，並利用分塊fcm聚類方法對建築群進行預分割，從而縮小了在航空影像數據中建築物提取的搜索空間，大大較少了計算量。
  19. To solve the problems of adaptive determinition of the cluster number, flexible objective function definition and approximate optimal computation in clustering analysis, a ga - based divisive hierarchical clustering algorithm ( gadhc ) was proposed, which integrated some features of divisive hierarchical clustering algorithm and binary genetic clustering algorithm

    摘要針對聚類中自適應確定聚類個數、目標函數靈活定義及優化的近似計算等問題，綜合了分裂式層次化聚類演算法能根據相似度閾值自適應地確定聚類個數的特點及二進制遺傳聚類演算法具有較強的搜索近似最優解能力及目標函數定義靈活的特點，提出了一種基於遺傳演算法的分裂式層次化聚類方法。
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