《文聚》,Wen Ju
1)Wen Ju《文聚》
英文短句/例句
1.Yunnan Literature of 1940s and Wen Ju of South-west United University西南联大《文聚》杂志与云南40年代文学
2.Synthesis and characterization of a series of Polysulfone (PSF)-Polyesters (PEs) block copolymers were undertaken.本文合成了一系列聚砜—聚酯类嵌段共聚物。
3.Zoom in on the learner and zoom in on the text;聚焦学习者 聚焦文本——《综合教程》简介
4.Two-Stage Text Clustering Based on Collaborative Clustering基于协同聚类的两阶段文本聚类方法
5.The Gathering and Spread Analysis on China Sci-Technique Journals Literature Cite我国科技期刊文献引文的聚散性分析
6.The Study on Feature Selection Algorithm in Chinese Text Clustering;中文文本聚类中特征选择算法的研究
7.A Study of Chinese Text Summarization Based on Adaptive Clustering Algorithm;基于自适应聚类的中文自动文摘研究
8.On the Lost Essay Seasons: the 4th Volume of Book Class of Poems and Essays;《艺文类聚》卷四“岁时部”阙文考
9.Yi Wen Lei Ju and Chu Xue Ji in Relation with LiteraryConception at the Early Tang Dynasty;《艺文类聚》、《初学记》与唐初文学观念
10.The Research on Graph Structure Representation Method Based Chinese Text Clustering基于图结构的中文文本聚类方法研究
parision on Classification of the Poetry in Literary Selections and Literature and Art Flocking together《艺文类聚》与《文选》诗歌分类之比较
12.Chinese short messages text clustering algorithm based on Isomap基于Isomap的中文短信文本聚类算法
13.Application of ISOMAP for Cluster Analyses Of Chinese DocumentsISOMAP在中文文本聚类分析中的应用
14.An Immune Based Algorithm for Chinese Network Short Text Clustering基于免疫的中文网络短文本聚类算法
15.A cording to the new cluster analysis a systematic clyster for the Chinese cape hare, Lepus capensis has been presented in this paper (Fig. 2).本文用新的聚类方法,对中国草兔进行系统聚类。
16.Research and Application of Combinatorial Clustering Methods in the Text Clustering组合聚类方法在文本聚类中的应用研究
17.Chinese search result clustering based on key noun phrase clustering基于关键名词短语聚类的中文搜索结果聚类
18.A Study of Chinese Multi-document Summarization Based on Adaptive Clustering Algorithm;基于自适应聚类的中文多文档自动文摘研究
相关短句/例句
document clustering文献聚类
1.Alldocument clustering methods are based on keywords now.现有的文献聚类方法都是通过文献关键词来进行的。
3)text clustering文本聚类
1.Webtext clustering algorithm based on the nonstructural data mining model;基于非结构化数据挖掘结构模型的Web文本聚类算法
2.Ontology-based similarity measure fortext clustering;文本聚类中基于本体的相似性测度(英文)
3.Topic discovery based ontext clustering techniques;基于文本聚类技术的主题发现
4)document clustering文本聚类
1.Border distance based multi-vectordocument clustering method;基于边界距离的多向量文本聚类方法
2.An improved k-means algorithm fordocument clustering;一种用于文本聚类的改进k-means算法
3.Variant of K-means algorithm fordocument clustering: optimization initial centers;一种优化初始中心点的K平均文本聚类算法
5)document clustering文档聚类
1.Research ondocument clustering algorithm of k-means based on aiNet;基于免疫网络的k-means文档聚类算法研究
2.Application of a non-linear dimension reduction algorithm ondocument clustering;非线性维数约减算法在文档聚类中的应用
3.Document clustering Applied to Search Results on the Web;文档聚类在Web搜索结果中的应用研究
6)text cluster文本聚类
1.The paper introduces the definition,characteristic of web text mining,emphatically analyzes the related technologies including text feature representation,characteristic subset selection,text classification,text cluster,and prospects the development of it.介绍了Web文本挖掘的定义、特点,重点分析了其相关的几种关键技术—文本特征表示、特征子选取、文本分类、文本聚类,并对其发展作以展望。
2.1)Propose atext clustering method based on GA-SOM-based Te.提出了以遗传算法优化SOM网络的文本聚类算法(GSTCA);进行了对比实验,实验表明,改进后的算法GSTCA比SOM算法在Web中文文档聚类中具有更高的准确率,其F-measure值平均提高了14%,同时,实验还表明,GSTCA算法对网络初始权值是不敏感的,从而提高了算法的稳定性。
3.On the basis oftext cluster,the author makes an exploratory research on XML documents cluster,through the improvement on partition cluster and layer cluster,makes them can use on XML documents cluster.本文在文本聚类的基础上对XML文档自动聚类进行了研究,对划分聚类法和层次聚类法进行了改进,使之适合于XML文档聚类;给出了元素比较法、边集比较法和编辑距离法等三种计算文档间相似度的方法,并利用实际数据进行了测试和分析。
延伸阅读
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