K-means algorithm is a classical clustering algorithm.
平均算法是经典的聚类算法。
This paper studies a combination clustering algorithm.
研究了一种聚类组合算法。
At this point, we are ready to run the clustering algorithm.
至此，我们已经可以运行这个群集算法了。
Clustering algorithm is an important one in data mining methods.
聚类算法是数据挖掘算法中的重要解决方法。
BIRCH algorithm is a clustering algorithm for very large datasets.
BIRCH算法是针对大规模数据集的聚类算法。
The dynamic clustering algorithm is developed to update the clusters.
最后针对数据库的更新设计了动态聚类算法。
This paper introduced a density grid-based data stream clustering algorithm.
提出了一种基于密度网格的数据流聚类算法。
Given a set of vectors, the next step is to run the k-Means clustering algorithm.
创建了一组矢量之后，接下来需要运行k - Means集群算法。
Select 3 as the maximum number of clusters and Kohonen as the clustering algorithm.
选择3作为集群的最大值并用Kohonen作为集群算法。
This paper presents a grid - based clustering algorithm for multi - density (GDD).
提出了一种多密度网格聚类算法gdd。
A new heuristic density-based ant colony clustering algorithm (HDACC) is presented.
提出一种基于密度的启发性群体智能聚类算法。
A fast clustering algorithm with adaptive density based on homogeneous grid is proposed.
提出了一种基于均匀网格的自适应密度快速聚类新算法。
The paper presents an ant colony clustering algorithm based on directional similarity: ACCADS.
提出了一种基于方向相似性度量的蚁群聚类算法。
A fault alarms correlation rule analysis model based on sequence clustering algorithm is designed.
设计了基于序列聚类算法的故障告警关联规则分析模型。
This paper proposes a rough spectral clustering algorithm and apply the algorithm on text data mining.
该文提出了一种粗糙谱聚类算法，并将其应用于文本数据挖掘。
Run the clustering algorithm of choice using one of the many Hadoop-ready driver programs available in Mahout.
使用Mahout中可用的 Hadoop 就绪的驱动程序运行所选集群算法。
In order to improve the efficiency we propose a distributed clustering algorithm based on large data sets.
为了提高聚类效率提出了一种基于分布式的大数据集聚类算法。
In this paper, the application of suppressed fuzzy clustering algorithm in image segmentation is introduced.
本文给出了模糊聚类算法在图像分割中的应用结果。
It is hard to cluster high-dimensional data using traditional clustering algorithm because of the sparsity of data.
在高维空间中，由于数据的稀疏性，传统的聚类方法难以有效地聚类高维数据。
Realize the clustering algorithm part of the recommendation system based on collaborative filtering and evaluate it.
对基于聚类的协同过滤推荐系统的聚类算法进行了实现和评价。
Next, based on the analysis of deficiency of BIRCH algorithm, we propose a new clustering algorithm based on sampling.
随后，在分析BIRCH算法不足的基础上，提出了一种基于抽样的聚类算法。
The cluster distance computing method is the key issue affecting the performance of hierarchical clustering algorithm.
在聚类的过程中簇间距离计算的准确性是影响算法性能的重要因素。
Then, for tradition matrix clustering algorithm carries on the optimization, improved as weight matrix cluster algorithm.
然后，对传统的矩阵聚类算法进行优化，改进为权值矩阵聚类算法。
The new clustering algorithm is analyzed on several aspects and tested on the published yeast cell-cycle microarray data.
从多方面分析了该算法的性能，并将该算法应用于酵母细胞周期的芯片表达谱数据聚类。
By analyzing ART2 neural network clustering algorithm, an improved ART2 neural network clustering algorithm was proposed.
分析了现有ART2网络存在的问题，提出了一种改进的ART2算法。
A novel clustering algorithm based on Bayesian model was introduced into the analysis of large-scale gene expression profiles.
在大规模基因表达谱的数据分析中引入了一种全新的基于贝叶斯模型的聚类算法。
InfoSphere Warehouse USES a statistical clustering algorithm to group customers that are similar in those two dimensions into clusters.
InfoSphereWarehouse使用一个统计集群算法将在这两个维上相近的客户分到集群中。
A dynamic clustering algorithm was proposed based on consistent matrix of dependent function for time series multi-dimensional data.
根据时序立体数据的特点，提出了基于关联函数一致性矩阵的动态聚类算法。
A dynamic clustering algorithm was proposed based on consistent matrix of dependent function for time series multi-dimensional data.
根据时序立体数据的特点，提出了基于关联函数一致性矩阵的动态聚类算法。
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