clustering ['klʌstəriŋ]

clustering的基本解释

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[变形] cluster的现在分词

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聚类

clustering的同义词

v. clump · cluster

与clustering相关的网络例句 [注：此内容来源于网络，仅供参考]

First, the character clustering algorithm regards the character as our features and then it can cluster these sentences of our target words to the correct group without any other resources. The average precision is 66.1%. Second, in the concept clustering algorithm of the aggregate computing, we use HowNet as the knowledge base of our feature words and obtain the concepts of these words. We cluster the sentences which have the same or similar concepts of the feature words into the same group. And then we can complement some lacks of the character clustering algorithm. The average precision is 72.3%. Third, regarding the concept clustering algorithm of the sememe distance, we use the sememe distance to compute its concept similarity. It improved similarity measure of the concept clustering algorithm of the aggregate computing. It achieves 81% average precision and gets better cluster quality.

词形分群演算法不受语料资源限制，能将词形相似且词义相近的词汇所属的句子分到同一群，经过人工验证，得到了66.1%的平均正确率；基於集合计算的概念分群演算法使用了知网做为撷取特徵词汇的知识库，透过知网取得词汇的概念，将具相同或相似概念的特徵词汇所属的句子分成同一群，补足词形分群演算法的不足，得到72.3%的平均正确率；基於义原距离的概念分群演算法则利用义原间的距离计算特徵概念的相似度，进一步改善了基於集合计算的概念分群演算法在相似度衡量的问题，得到81%的平均正确率，达到更好的分群效果。

Researches on kernel clustering algorithms. After combining K-means clustering algorithm and the theory of kernel-based learning algorithms, we propose a fast kernel K-means clustering method which is based on CPD kernel. The experiment results indicate that the clustering effect of the algorithm is better than that of K-means algorithm, the clustering speed of the algorithm is also fast than that of K-means algorithm.

核聚类算法的研究：探讨了K-均值聚类算法，通过将核学习理论与K-均值聚类算法结合，提出了一种基于CPD核函数的快速核K-均值聚类算法，并将该算法与基于Mercer核的核聚类算法进行了比较，实验结果显示，我们的方法不仅比K-均值聚类算法的聚类效果好，而且聚类速度快。

A new hybrid clustering algorithm in phase of high-efficiency and good quality was put forward on the foundation of synthetically analyzing K-means clustering algorithm based on partition and agglomerative clustering algorithm based on hierarchy, and consulting some improved hybrid clustering algorithms.

在综合分析基于划分的K均值聚类算法和基于层次的凝聚聚类算法的基础上，借鉴各种混合聚类方法，提出了一种执行效率更高和聚类质量更好的分阶段混合聚类算法。

与clustering相关的网络解释 [注：此内容来源于网络，仅供参考]

clustering：聚类

同时还采取了一个称之为"聚类"(clustering)的新技术来彻底杜绝作弊现象. 另一家著名搜索引擎公司Excite更干脆,对网站标引时忽略Meta标签,以防止作弊. 但该做法对其他作弊现象,如大量重复关键词等则无能为力. 而且这种以噎废食的做法也不值得提倡.

clustering：群集

在 IM Scoring 中,挖掘函数被分为几组不同的挖掘类型: 群集(Clustering)、分类(Classification)和 回归(Regression). 记分(Scoring)函数可以使用其中每一种类型. 每个记分函数包含了不同的算法,用以处理该类型中包括的不同挖掘函数.

clustering：集群

更高, 更陡的尖峰还留在前面, 比如 "集群"(clustering), "分布式文件系统"(distributed file system), "无处不在的计算"(ubiquitous computing). 并不是说这些"山头"有多么新, 而是现有的解决方案需要相当的发展.

clustering：聚集

上图流程,简单的说就是把交易系统已经发生过的数据,通过ETL工具抽取到主题明确的数据仓库中,OLAP后生成Cube或报表,透过Portal展现给用户,用户利用这些经过分类(Classification)、聚集(Clustering)、描述和可视化(DescriptionandVisualization)的数据,

clustering agent：群集剂

clustering 群集,成簇,成束 | clustering agent 群集剂 | cnidarian toxin 海葵毒素