例句與造句

1. Hierarchical clustering method
   系統(tǒng)聚類法
2. In the field of clustering , by comparing some clustering methods and analysing characteristic of science data , we propose an improved hierarchical clustering method synthetic idea of k - means method
   在聚類方面,經(jīng)過比較各種聚類算法和分析科學(xué)數(shù)據(jù)的特點,提出了結(jié)合k -平均思想的改進(jìn)型系統(tǒng)聚類算法。
3. Then the methods of ontology integration is studied , which falls into two main steps : using hierarchical cluster method to find similar concepts and using heuristic rule to merging similar discovered concepts
   本文接著對本體集成方法進(jìn)行詳細(xì)研究。本體集成過程為先利用聚類算法來找出相似概念,再利用啟發(fā)式規(guī)則進(jìn)行相似概念合并處理。
4. It learns from the basic thought of hierarchical clustering methods ( hcm ) , which groups objects by comparing the sizes of distance or similar coefficients between objects , meanwhile , combines with the optimal split - plot designs ( ospd ) in ordered samples , and synthesizes the intuitive property of hcm and the character of simplicity and the ability in finding out the accurate solution of ospd . with history data , this paper assumes that data from the same group come from the same distribution , and so do the history data
   本文汲取了系統(tǒng)聚類法中通過定義距離或相似系數(shù)并以其大小將對象進(jìn)行分類的基本思想，將之與有序樣本情況下的最優(yōu)分割法相結(jié)合，吸收了系統(tǒng)聚類法的直觀性和最優(yōu)分割法的簡捷性及可以求出精確最優(yōu)解的良好性質(zhì)，在存在歷史數(shù)據(jù)的條件下，假設(shè)同類數(shù)據(jù)來自于同一分布，歷史數(shù)據(jù)相應(yīng)的來自該分布。
5. The basic idea for hierarchy - based method is that creating and maintaining a tree of clusters and sub - clusters according to some kind of criterion to measure the distance of clusters , the procedure will be sloped until some terminal conditions are satisfied . hierarchical clustering method can be further classified into agglomerative and divisive hierarchical clustering , depending on whether the hierarchical decomposition is formed in a bottom - up or top - down fashion . most hierarchical clustering methods can produce the better results when the clusters are compact or spherical in shape . but they do not perform well if the clusters are any shape or there are outliers . a main reason is that the most hierarchical clustering methods employ medoid - based measurement as distance between clusters
   基于層次方法的聚類的基本思想足：根據(jù)給定的簇間距離度量準(zhǔn)則，構(gòu)造利維護(hù)一棵由簇利子簇形成的聚類樹，直至滿足某個終結(jié)條件為止。根據(jù)層次分解是自底向上還是自頂向下形成，層次聚類方法可以分為凝聚的( agglomerative )和分裂的( divisive ) 。人多數(shù)層次聚類算法在緊密簇或球形簇結(jié)構(gòu)下能夠產(chǎn)生較好的聚類效果。
6. It's difficult to find hierarchical clustering method in a sentence. 用hierarchical clustering method造句挺難的