k-means cluster中文什么意思
类中心聚类
逐步聚类分析
例句与用法
更多例句： 上一页 下一页- The euclidean distance is usually chosen as the similarity measure in the conventional k - means clustering algorithm , which usually relates to all attributes
传统的k -均值算法选择的相似性度量通常是欧几里德距离的倒数，这种距离通常涉及所有的特征。 - This paper studies using the k - means clustering algorithm to classified the obtained image , submits through two phases to retrieve the image and adjusts the weight using the relevant feedback method
本文研究利用k均值聚类方法对检索得到的图像进行分类,通过两阶段提交对图像进行筛选,并利用相关反馈方法来调整权重。 - The property of the recall - precision curve of a general retrieval algorithm and the k - means clustering method are used to realize the expansion according to the distance of image features of the initially retrieved images
扩展主要利用了一般检索算法的查准率查全率曲线特点，对原始查询结果的图像特征距离应用k -均值聚类算法，确定多个查询示例图像。 - Finally , according to the practical requirement of classification management to credit risk management , it uses k - means clustering method to cluster the evaluation result , and then get the credit ranks the small and middle enterprises belong to
最后,根据对信用风险管理应实行分级管理的实践要求,利用k -平均聚类划分法对信用风险评估结果进行聚类划分,从而得到各中小企业所属于的信用风险等级。 - In this text , we first do some research on the genetic algorithm about clustering , discuss about the way of coding and the construction of fitness function , analyze the influence that different genetic manipulation do to the effect of cluster algorithm . then analyze and research on the way that select the initial value in the k - means algorithm , we propose a mix clustering algorithm to improve the k - means algorithm by using genetic algorithm . first we use k - learning genetic algorithm to identify the number of the clusters , then use the clustering result of the genetic clustering algorithm as the initial cluster center of k - means clustering . these two steps are finished based on small database which equably sampling from the whole database , now we have known the number of the clusters and initial cluster center , finally we use k - means algorithm to finish the clustering on the whole database . because genetic algorithm search for the best solution by simulating the process of evolution , the most distinct trait of the algorithm is connotative parallelism and the ability to take advantage of the global information , so the algorithm take on strong steadiness , avoid getting into the local
本文首先对聚类分析的遗传算法进行了研究,讨论了聚类问题的编码方式和适应度函数的构造方案与计算方法,分析了不同遗传操作对聚类算法的性能和聚类效果的影响意义。然后对k - means算法中初值的选取方法进行了分析和研究,提出了一种基于遗传算法的k - means聚类改进(混合聚类算法) ,在基于均匀采样的小样本集上用k值学习遗传算法确定聚类数k ,用遗传聚类算法的聚类结果作为k - means聚类的初始聚类中心,最后在已知初始聚类数和初始聚类中心的情况下用k - means算法对完整数据集进行聚类。由于遗传算法是一种通过模拟自然进化过程搜索最优解的方法,其显著特点是隐含并行性和对全局信息的有效利用的能力,所以新的改进算法具有较强的稳健性,可避免陷入局部最优,大大提高聚类效果。
