First, the whole system was decomposed into several subsystems by adopting fuzzy k-means cluster.
首先，采用动态聚类方法，将整个系统分解为几个子系统。
Clustering analysis has been used in many field of life. 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。
At last, the segmentation result is clustered again using K-means cluster to get the ultimate segmentation result.
最后用K均值算法对谱聚类集成的结果进行再次聚类，得到最终的集成聚类分割结果。
A recognition method based on HMM and K-means cluster is proposed through extracting LPC characteristic from acoustic target.
提出一种隐马尔可夫模型和K -均值聚类混合模型的声目标识别方法。
I use mathematical statistics analysis, such as confidence intervals, hypothesis testing, K-means Cluster, regression analysis and so on.
运用置信区间分析、假设检验、聚类分析、回归分析等数理统计分析方法。
Firstly, RAC and K-means clustering method are combined in this algorithm by the way of searching pre-matches feature points, which are called the cluster point set, of the unknown model.
此算法首先结合RAC和K -均值聚类方法对未知模型的特征点进行预匹配，得到的匹配结果称为聚类点集。
Using K Means which can automatically cluster trajectories, a new algorithm based on trajectory space similarity distance is presented, and it is applied to classify trajectory.
应用K均值自动聚类算法，提出了一种新的基于轨迹空间相似距离的轨迹分类算法，对以上获得的有效轨迹进行分类。
Lastly, the experiments proved the method of image segmentation based on cloud model is better than the method based on fuzzy K means cluster and the method based on K means.
最后，试验证实了图像分割办法基于云模型比该办法基于模糊凯西意味着聚类和办法基于凯西的办法。
In cluster analysis, Fuzzy K-Means (FKM) algorithm is one of the most widely used methods. However, FKM algorithm is much more sensitive to the initialization, and easy to fall into local optimum.
在聚类分析中，模糊k均值算法是目前应用最为广泛的方法之一，然而该算法对初始化敏感，容易陷入局部极值点。
The experimental result shows that the K-means with the proposed technique can produce cluster results with high purity as well as good stableness.
实验表明，该算法能够生成质量较高而且波动性较小的聚类结果。
Firstly, the paper deal with the EST sequence data in a certain way; secondly, we cluster the processed data by K-means algortihm to get clusters;
论文首先是数据预处理，其次，对处理后的数据进行K-均值聚类，获得一个粗略的聚类；
After analyzing the traditional clustering algorithms, the paper presents a new clustering ensemble method based on K-means to cluster data.
本文在分析传统聚类算法的基础上，提出了一种聚类融合算法。
After analyzing the traditional clustering algorithms, the paper presents a new clustering ensemble method based on K-means to cluster data.
本文在分析传统聚类算法的基础上，提出了一种聚类融合算法。
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