term clustering
英 [tɜːm ˈklʌstərɪŋ]
美 [tɜːrm ˈklʌstərɪŋ]
网络 词聚类; 词聚类方法; 项聚类
双语例句
- A Single-Word Term Recognition Approach Based on FCM Clustering Algorithm
基于FCM聚类算法的单词型术语识别方法 - A Study of Term Expressing and Clustering Method of Text Mining
文本挖掘中的特征表示及聚类方法 - The Duplex Strategy of Term Weighting in Text Clustering
文本聚类中权重计算的对偶性策略 - This method uses frequent term set clustering to compress massive data and uses semantic information to improve accuracy.
该算法使用频繁词集聚类来压缩数据，并使用语义信息进行分类。 - Document clustering approach based on term clustering and association rules
一种基于术语簇和关联规则的文档聚类方法 - Mid-long term load forecast of power system is affected by various uncertain factors and research shows that clustering method can synthesize numerous relative factors and put them into forecast model.
中长期电力系统负荷预测受大量不确定因素的影响，研究表明聚类方法能够将各种影响因素综合引入预测模型。 - Study on term clustering techniques
词聚类方法研究 - Novel method of frequent term set s-based text clustering was presented.
提出了基于频繁特征项集的文档聚类方法。 - A model for short term and super short term forecasting integrating neural network, expert system and dynamic clustering is introduced here, which involves weather, festival and other load forecasting affecting factors.
介绍了一种整合神经网络、专家系统和动态聚类多种智能方法为一体的短期/超短期预测模型，综合考虑了气象、节假日等负荷影响因素。 - Power System Short Term Load Forecasting Based upon a Combination of Markov Chain and Fuzzy Clustering
基于马尔可夫链和模糊聚类的电力系统短期负荷预测 - Massive short documents classification method based on frequent term set clustering
基于频繁词集聚类的海量短文分类方法 - The paper also analyzes the algorithm of statistics-based term extraction, normalization, term-document matrix construction, singular value decomposition, and hierarchical agglomerative clustering.
接着，本文讨论了这三个步骤所涉及的算法，包括基于统计模式对文本抽词、基于奇异值分解从词&文档矩阵中提取本体、基于语义相似度对本体进行聚类等。 - An iteration method is proposed to deal with the duplex phenomena found in term weighting and compute out the latent concept. Experimental results show that the latent concept could help to get better clustering results.
利用迭代的方法来处理和利用这种对偶性，获得了文本的隐含概念.实验结果表明，采用概念空间代替原始词空间来表示文本，能够得到更好的聚类结果。 - On the basis of an improved semantic affinity based term clustering algorithm, a semantic-based clustering approach for concept generation is proposed. Domain concepts are generated through semantic affinity based term clustering while terms are extracted from term sets of heterogeneous knowledge sources.
改进了基于语义相似度的术语聚类算法，在此基础上提出了基于语义聚类的概念生成方法，其基本思想是由各知识源的术语集合，通过基于语义相似度的术语聚类，生成领域概念。 - First of all, the pairwise constraint sets are found out in feature term set. After that the constraint sets can be expanded by using the K-nearest neighbors set method, and clustering according to the partition results of the constraint set.
此方法是通常先找出特征词库中的成对约束集，再用K最近邻近集的方法对成对约束集进行扩充，聚类后将每个簇中的特征词合并成一个新的属性。 - As the result of enriching the linguistical rules, the coverage of rules is enlarged and the recall is improved. Secondly, a single-word term recognition approach based on fuzzy clustering is proposed, according to the simple structure and the unambiguous boundary.
丰富语言规则的同时，扩大了规则覆盖面，提高术语抽取的召回率。2.针对单词型术语结构简单，边界清晰的特征，提出一种基于模糊聚类的识别算法。 - Finally, an information retrieval system based on the LSI is implemented, the term transfer relation for the information retrieval initial results are selected, and the results by document clustering algorithm are adjusted.
最后本文实现了一个基于潜在语义索引的文本检索系统，对检索的初始结果进行特征传递关系选择并通过聚类手段调整检索结果。