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cluster process中文什么意思

发音:   用"cluster process"造句
  • [数] 簇生过程
  • a cluster:    自力村碉楼群
  • a cluster of:    一串/丛; 一串,一束,一群; 一群、一组; 一群;一组
  • cluster:    n. 1.丛集;丛;(葡萄等的)串,挂;(花)团;(秧)蔸;组。 2.(蜂、人等的)丛,群,群集。 3.【物理学】聚集,组件;【化学】类族,基;(原子)团;【天文学】星团。 4.【美军】(表示又一个同等勋章的)金属片。 5.【语音】音丛,音群,义丛,词组。 6.集中建筑群〔在一大片土地上集中兴建住宅,以提供较大的公共休息场所〕。 in a cluster 成串的;成团[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
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例句与用法

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  1. 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:只要邻近区域的密度(对象或数据点的数目)超过某个阈值,就继续聚类。
  2. 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
    聚类分析依据的原则是使同一聚簇中的对象具有尽可能大的相似性,而不同聚簇中的对象具有尽可能大的相异性,聚类分析主要解决的问题是如何在没有先验知识的前提下,实现满足这种要求的聚簇的聚合。
  3. 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
    但是仍然存在几个问题尚待解决:首先,聚类时怎样自动确定簇的个数和半径;其次,如何利用含有多个正规化参数的局部岭回归方法进行rbf网络学习;第三,如果簇的形状是不规则的,则它很难用径向基函数来描述,因此需要研究其它能代表不规则形状的簇的基函数。
  4. 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
    聚类分析是在没有先验知识支持的前提下,根据事物本身的特性研究被聚类对象的类别划分,实现满足这种要求的类的聚合,它所依据的原则是使同一类中的对象具有尽可能大的相似性,而不同类中的对象具有尽可能大的差异性。
  5. 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
    正是由于聚类分析的重要性和特殊性,近年来在该领域的研究取得了长足的发展,涌现出了许多聚类分析的方法,如划分聚类方法(partitioningmethod)、层次聚类方法(hierarchicalmethod)、基于密度(density?based)的聚类方法、基于网格(grid?based)的聚类方法、基于模型(model?based)的聚类方法等等。

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