cluster process中文是什么意思

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中文翻譯
造句

[數(shù)] 簇生過程

a cluster 自力村碉樓群
a cluster of 一串/叢; 一串，一束，一群; 一群、一組; 一群；一組
cluster n. 1.叢集；叢；(葡萄等的)串，掛；(花)團(tuán)；(秧)蔸；組。 2.(蜂、人等的)叢，群，群集。 3.【物理學(xué)】聚集，組件；【化學(xué)】類族，基；(原子)團(tuán)；【天文學(xué)】星團(tuán)。 4.【美軍】(表示又一個(gè)同等勛章的)金屬片。 5.【語音】音叢，音群，義叢，詞組。 6.集中建筑群〔在一大片土地上集中興建住宅，以提供較大的公共休息場所〕。 in a cluster 成串的；成團(tuán)[群]的。 vi.,vt. (使)成群；(使)群集。 clustered column 【建筑】簇柱。
a cluster of buildings 一群建筑物
a cluster of butterflies 一群蝴蝶
a cluster of flaws 密集區(qū)缺陷
a cluster of grapes 葡萄
a cluster of islands 一群海島
a cluster of stars 一個(gè)星團(tuán)
abell cluster 阿貝爾星系團(tuán)
aggression cluster 精神沖擊彈
arches cluster 圓拱星團(tuán)
argolis cluster 阿勾里斯星群
aromatic cluster 芳香族稠環(huán)
artificial cluster 人為群體
association cluster 關(guān)聯(lián)簇
atas cluster 先進(jìn)技術(shù)通報(bào)系統(tǒng)處
atom cluster 金屬膠態(tài)原子簇
attitude cluster 態(tài)度叢
banana cluster 香蕉串
barchan cluster 新月沙丘群
barrel cluster 炮管組
base cluster 基簇; 基組
beehive cluster 鬼星團(tuán)
bezel cluster 表框

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例句與用法

Birch algorithm . density-based method : if the density of neighborhood, that is the number of data objects, exceeds a certain value, the clustering process will be continued
基于密度的方法的主要思想是：只要鄰近區(qū)域的密度(對象或數(shù)據(jù)點(diǎn)的數(shù)目)超過某個(gè)閾值，就繼續(xù)聚類。
Dissimilarities are assessed base on the attribute values describing the objects . clustering processes are always carried out in the condition without pre-known knowledge, so the main task is to solve that how to get the clustering result in this premise
聚類分析依據(jù)的原則是使同一聚簇中的對象具有盡可能大的相似性，而不同聚簇中的對象具有盡可能大的相異性，聚類分析主要解決的問題是如何在沒有先驗(yàn)知識的前提下，實(shí)現(xiàn)滿足這種要求的聚簇的聚合。
However, there have still some unresolved problems : first, how to determine the number and size of the clusters automatically during the clustering process . second, how to utilize the " local " ridge regression method which including multiple regularization parameters in learning rbf network . third, those clusters in irregular form ca n't represented by radial basis function, thus we must find some other basis functions that can describe the irregular form
但是仍然存在幾個(gè)問題尚待解決：首先，聚類時(shí)怎樣自動確定簇的個(gè)數(shù)和半徑；其次，如何利用含有多個(gè)正規(guī)化參數(shù)的局部嶺回歸方法進(jìn)行rbf網(wǎng)絡(luò)學(xué)習(xí)；第三，如果簇的形狀是不規(guī)則的，則它很難用徑向基函數(shù)來描述，因此需要研究其它能代表不規(guī)則形狀的簇的基函數(shù)。
Clustering analysis is the method which partition class to the clustered objects as required of thing's characteristics . clustering processes are always carried out in the condition with no pre-known knowledge, so the most research task is to solve that how to get the clustering result in this premises
聚類分析是在沒有先驗(yàn)知識支持的前提下,根據(jù)事物本身的特性研究被聚類對象的類別劃分,實(shí)現(xiàn)滿足這種要求的類的聚合,它所依據(jù)的原則是使同一類中的對象具有盡可能大的相似性,而不同類中的對象具有盡可能大的差異性。
clustering processes are always carried out in the condition with no pre-known knowledge, so the most research task is to solve that how to get the clustering result in th is premise . as the development of data mining, a number of clustering algorithms has been founded, in general, major clustering methods, can be classified into the following categories : partitioning methods; hierarchical methods; density-based methods; grid-based methods; model-methods; besides these, some clustering algorithms integrate the ideas of several clustering methods
正是由于聚類分析的重要性和特殊性，近年來在該領(lǐng)域的研究取得了長足的發(fā)展，涌現(xiàn)出了許多聚類分析的方法，如劃分聚類方法(partitioningmethod)、層次聚類方法(hierarchicalmethod)、基于密度(density?based)的聚類方法、基于網(wǎng)格(grid?based)的聚類方法、基于模型(model?based)的聚類方法等等。
And it adds a-priori information into the patterns to change the method as a semi-supervised clustering . in the clustering process, the unlabelled patterns compare similarities with the labeled patterns, and then the accuracy of the algorithm can be increased . ( 3 ) the paper proposes an interactive learning-based image mining in remote sensing
由于遙感圖像各類別在特征空間中散點(diǎn)圖的分布的特點(diǎn),本文對傳統(tǒng)的fcm聚類算法進(jìn)行改進(jìn),并且加入先驗(yàn)信息之后,將原來的非監(jiān)督的聚類變成一種半監(jiān)督的聚類方法,通過與已標(biāo)簽的樣本進(jìn)行相似性比較,能有效地提高聚類算法的準(zhǔn)確度。

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