This paper , based on data processing, gives one model of teaching quality evaluating by means of grey clustering analysis.
在数据处理的基础上，利用灰色数学及聚类分析的方法给出了一种教师教学工作质量评估的模型。
Attribute means clustering is more robust than fuzzy means clustering by theoretical analysis and numerical example.
通过理论分析，属性均值聚类是比模糊均值聚类更稳健的聚类方法。
By means of analyzing kernel clustering algorithm and rough set theory, a novel clustering algorithm, rough kernel k-means clustering algorithm, was proposed for clustering analysis.
通过研究核聚类算法，以及粗糙集，提出了一个新的用于聚类分析的粗糙核聚类方法。
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。
Calculation by means of previous forecast factors can first make grey clustering analysis possess forecast function.
文章首次利用前期的预报因子进行计算，使灰色聚类分析具有了预报功能。
And then factor scores to cluster analysis and multiple comparison of means to observe the different groups of people clustering of the different conditions and a stable condition.
然后对因子得分进行聚类分析和多重均值比较，观察不同人群聚类状况及稳定状况之不同。
Clustering is an important means to analyze genetic data. This paper focuses on the cluster analysis of gene chip data, including partitioned clustering and pattern-based approach.
聚类分析是基因数据分析中的一种重要手段，本文主要内容包括基于划分的聚类算法的改进以及模式聚类的理论研究。
This paper introduces an intrusion detection model based on clustering analysis and realizes an algorithm of K-means which can set up a database of intrusion detection and classify safe levels.
提出了一种基于聚类分析方法构建入侵检测库的模型，实现了按k -平均值方法建立入侵检测库并据此划分安全等级的思想。
This paper proposed a novel hybrid algorithm for clustering analysis based on artificial fish-school algorithm and K-means.
结合人工鱼群算法的全局寻优优点提出了一种基于人工鱼群算法的K -平均混合聚类分析算法。
Researched the unsupervised anomaly detection methods based on clustering analysis, improved the K-means algorithm.
研究了基于聚类分析的非监督式异常检测方法，并改进了K均值算法用于聚类分析。
Aiming at solving the asymmetry of the distribution of particles, we classified the particles in images by means of density clustering analysis.
并且针对图像中粒子分布的不均匀性，提出了利用密度聚类分析对图像中的粒子密度进行分类。
Conclusion the clustering analysis method, as an ideal means, may provide detailed and reliable results in analyzing the serum protein profile from SELDI.
结论本实验采用的聚类分析法获得的结果详尽、可靠，是分析SELDI蛋白芯片等大规模实验数据的理想工具。
Conclusion the clustering analysis method, as an ideal means, may provide detailed and reliable results in analyzing the serum protein profile from SELDI.
结论本实验采用的聚类分析法获得的结果详尽、可靠，是分析SELDI蛋白芯片等大规模实验数据的理想工具。
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