k-mean的例句大全

I selected matlab fuzzy logic toolbox to train the classifier, meanwhile, in order to contrast my experiment, I also selected K-means algorithm to be experimented based on Weka data mining software.

选择基于matlab的模糊逻辑工具箱来训练分类器，同时为了进行对比实验，基于Weka数据挖掘软件进行了K-means聚类实验。

However, the time required in a direct algorithm of k-means method is sensitive to the number of patterns.

但是，k-means直接算法的时间复杂度和模式复杂度对数据量的大小非常敏感，无法满足一些高性能的应用场合，如个性化服务中对用户数据进行的群组分析。

The performance of K-means clustering algorithm depends on the selection of distance metrics.

K-均值（K-means）算法聚类的结果依赖于距离度量的选取。

The paper discusses self-organizing feature map ( SOM), K-means clustering and a cluster validity measure, Silhouette index.

文章讨论了自组织映射、K平均值聚类和一种有效性测度Silhouette指数。

Three clustering algorithms, namely K-means, Self-Organizing Map ( SOM) and Affinity Propagation algorithm and two combined strategies ( DCM and Cosine) are compared and analyzed in the thesis.

本文对比研究了三种聚类算法，即K-means、自组织映射和Affinity Propagation算法，以及两种合并策略（DCM和Cosine）。

When the noise is severe such as 9%, the segmentation accuracy of K-means clustering algorithm, fuzzy C means and method based on Gaussian mixture model decrease 4% averagely.

当噪声程度达到9%时，K均值法、模糊C均值法和高斯混合模型的分割准确率平均下降了4%。

Firstly, on bisecting K-means is used to quantize image roughly and then we refine the image by improved spectral clustering based weighted distance.

首先利用高效的二分K均值聚类进行粗略量化，然后使用基于加权距离的谱聚类进行再次量化。

There are lots of drawbacks to traditional incremental K-means in event detection.

传统的增量k均值法用于事件探测时存在着诸多不足。

Application of an improved k-means algorithm based on outliers and original clustering center

基于孤立点和初始质心选择的k均值算法的改进与应用

Hybrid Particle Swarm Optimization employing improved k-means clustering analysis strategy

采用改进的k均值聚类分析策略的粒子群算法

An Effective Distributed k-Means Clustering Algorithm Based on the Pretreatment of Vectors' Inner-Product

基于向量内积不等式的分布式k均值聚类算法

Application of k-means clustering analysis in process improvement

k均值聚类分析在过程改进中的应用

The two fuzzy clustering algorithms are mainly analyzed: fuzzy clustering algorithm based on equivalence relation and fuzzy k-means clustering algorithm.

重点分析了两种常用的模糊聚类算法：基于等价关系的模糊聚类和模糊k均值聚类。

K-means algorithm possesses strong local search capability and rapid convergence capability, and the clustering result is independent of the order of the sample data.

k均值算法的局部搜索能力强、收敛速度快，且聚类结果不受样本数据输入顺序的影响。

After analyzing the disadvantages of the classical k-means clustering algorithm, this paper proposes a novel hybrid clustering algorithm incorporating Particle Swarm Optimization into k-means algorithm.

本文在分析k均值聚类算法存在问题的基础上，用粒子群算法优化k均值聚类算法，提出了一种新的混合聚类算法。

Because of false classification of feature samples by k-means clustering analysis, a kernel based dynamic clustering method was presented to distinguish different working modes of an air compressor.

针对k均值聚类对特征样本划分存在误分类的问题，提出用基于核的动态聚类算法对风机不同工作状态进行分类识别。

In this method, k-means algorithm is employed for finding the clusters of each class, thus the final recognition results are unstable, not optimal and depend on the initial cluster centers.

然而，由于该方法采用的子类划分方法是k均值聚类算法，因而不能保证最终的识别结果是稳定的、最优的、且依赖聚类初始中心的选择。

In order to overcome its shortcomings, this paper proposed an improved incremental K-means ( IIKM) for detecting events.

为了克服其缺陷，本文提出了一种用于事件探测的改进的增量k均值算法（IIKM）。

The Bag of Words framework based on SIFT was analysised. The support vector machine was used for feature selection point of the visual vocabulary. The experiments show better performance than the k-means clustering algorithm.

以此为基础，研究了SIFT的词袋算法框架，通过支持向量机选择视觉词汇的特征点，实验表明，性能优于k均值聚类算法。

EM clustering algorithm is a popular iterative refinement algorithm which can be regarded as an extension of K-means algorithm. Based on the probability of the occurrence of subordination between objects and clusters, the objects can be allotted by this algorithm.

EM聚类算法是一种流行的迭代求精算法，可以看作是k均值算法的一种扩展，该算法根据对象与簇之间的隶属关系发生的概率来分配对象。