K-Means
网络释义：算法；K均值；聚类
网络释义
1.算法 K-MEANS 算法(K均值算法) k-means 算法 ...
2.K均值 最近邻分类器： K-NN K均值： K-means 拟合直线： fitting pne ...
3.聚类 ACE-value 评测 K-means 聚类 phase-portrait 近似 ...
4.k均值聚类 [转载]向量空间模型( VSM) k均值聚类( K-means) 分类算法之决策树( Decision tree) ...
5.聚类算法 ... ·聚类分析( Clustering Analysis) ·K-均值算法( K-means) ·基于主机的入侵检测系统( HIDS) ...
7.算法实现 Apriori 算法 k-means 算法实现 k-means 算法 ...
例句释义:,算法,聚类1.Clustering analysis has been used in many field of pfe. K-Means cluster is classic partitioning Clustering.聚类分析已经被广泛地应用于生活中的各个领域。
2.The action potentials' features are extracted by PCA, the action potential classification is implemented by the improved K-means algorithm.该方法采用PCA提取动作电位特征，使用改进K均值算法实现动作电位分类。
3.LEACH routing protocol for the lack of a k-means clustering based on the multi-hop routing algorithm clustering LEACH-KMCM.针对LEACH协议的不足，提出了一种基于k均值聚类的多跳分簇路由算法LEACH-KMCM。
4.A wide range of existing K-means clustering method to select the initial cluster centers have their own advantages and disadvantages.现有的种类繁多的K-均值聚类的初始聚类中心选取方法有各自的优缺点。
5.CONCLUSIONS: K-means objectively separates contractile and non-contractile tissue components.结论：K-均值能够客观地区分收缩组织和非收缩组织成分。
6.Common approaches to unsupervised learning include k-Means, hierarchical clustering, and self-organizing maps.无监管学习的常见方法包括k-Means、分层集群和自组织地图。
7.First we make a loose classification with k-means clustering algorithm to fix a category of interest.先用-均值聚类算法作粗糙划分，确定感兴趣类。
8.The result shows that k-means cluster isn't suitable for indicators' classification and factor analysis is a good classification method.指出聚类分析作为一种公认的分类方法，在期刊评价指标分类中并不适用。
9.We use pairwise constraints k-means algorithm to cluster in the subset in which the closures are changed by closures center.在此基础上利用监督信息对原始数据进行降维，利用闭包中心代替闭包集，最后在基于成对约束的K均值算法上进行聚类。
10.The experiments indicate that the rough K-means based on self-adaptive weights is an effective rough clustering algorithm.实验结果表明，基于自适应权重的粗糙K均值算法是一种较优的聚类算法。