Fast Fractal Coding by Combining K-Mean Clustering and KD-Tree Search

A K-mean and Markov random field combined classification algorithm is presented for the coherence map classification.

提出了基于K-均值-Markov随机场的干涉SAR相关系数图组合分类算法。

The K-mean clustering algorithm is improved in class centers initialization for fast convergence.

同时对K-均值聚类算法的初始值设定进行了改进以提高收敛速度。

Segmentation of Caption Region Using Wavelet Transform and K-Mean Clustering

利用小波变换和K均值聚类实现字幕区域分割

Application of Fuzzy of K-mean Cluster Analysis on the Classification Research of Acid-base Properties of Compounds

模糊K均值聚类分析在化合物酸碱性分类研究中的应用

Metal Artifact Reduction in CT Using K-Mean Cluster Algorithm

基于K均值聚类算法的CT图像金属伪影去除

K-mean distance outlier factor detect for outlier pattern of time series

时间序列异常模式的k-均距异常因子检测

Micrograph Segmentation of Chinese Traditional Medicine Lamina Based on K-mean Clustering Algorithm

基于K-均值聚类算法的中药叶片显微图像分割

K-mean algorithm for optimizing the number of clusters based on particle swarm optimization

基于微粒群优化聚类数目的K-均值算法

The fast K-mean image segmentation algorithm based on the grey histogram

基于灰度直方图的快速K-MEAN图象分割算法

A Double k-Mean Clustering Algorithm for Sequential Gene Data Based on the Hidden Markov Model

基于隐马尔可夫模型的二次k-均值基因序列聚类算法

K-Mean Cluster Particle Swarm Optimization Algorithm Based on Self-adaptive Chaotic Mutation

基于自适应混沌变异的k-均值聚类粒子群算法

Research on Comparing the Sequential Learning with Batch Learning for K-Means

K-Means聚类中序列模式和批量模式的比较研究

A super-peer P2P model based on K-means clustering

一种基于K-means聚类分组的P2P超结点模型

K-means Clustering Based on Evolution Strategy

基于进化策略的K-means聚类算法

On the Improvement of K-means Clustering Algorithm

一种改进的K-均值聚类算法的研究

Improve k-means algorithms of cluster method by GA

用遗传算法改进聚类分析中的K-平均算法

Furthermore, Bezdek's fuzzy K means algorithms are computationally expensive so that they are impractical in codebook design. So, people have been researching those algorithms which can achieve good performance in the convergent speed of algorithms and the quality of the reconstructed image.

而Bezdek的模糊K-均值算法由于计算量很大，也很少用于矢量量化的设计码书，因此，人们一直在寻找收敛速度和收敛效果两者性能都较好的算法。

In this method, a feature vector is assembled, and an edge preserving filter is used for getting rid of the estimating error, feature space is firstly classified based on K means clustering, the final segmentation is accomplished by using probabilistic relaxation techniques.

在特征划分上，再运用边缘保持的图像滤波对得到的分形维数特征空间进行滤波，滤波结果用K均值簇分类法作特征空间的初分类；

The inter spectral redundancy and intra spectral redundancy can be eliminated mostly by multilevel clustering algorithm with quickly convergent K means classification and the method clearing redundancy at step through enhancing the intra class pixel redundancy.

多层次聚类无损压缩就是利用改进的K-means聚类算法具有快速收敛的特点，和利用分层次去冗余的方法来聚类，因此可最大限度消除残差冗余。

The algorithm uses the result of K means clustering to guide the initiation of fuzzy clustering so that the iteration number of fuzzy clustering can be reduced obviously, thus the speed of fuzzy clustering can be accelerated greatly.

该算法利用K均值聚类结果指导模糊聚类的初始化，使模糊聚类的迭代次数明显减少，从而极大地提高模糊聚类的速度。