文介绍了一种聚类大型二元数据集合的快速算法，在该数据集合中数据点是高维的，并且大多数的坐标值为零。
This paper introduces a fast algorithm to cluster large binary data sets where data points have high dimensionality and most of their coordinates are zero.
在公开数据集和人工数据集上的实验结果表明，DP算法能快速高效地找到接近于真实聚类中心的数据点作为初始聚类中心。
Experiments on both public and real datasets show that DP is helpful to find cluster centers near to real centers quickly and effectively.
顺序聚类算法是一种非常直接和快速的算法，并且不需要提前确定聚类个数。
Sequential algorithm is a straightforward cluster algorithm, and people do not have to provide the number of clusters in advance.
针对聚类算法的中心点问题，提出了相应的层次编码型数据的快速处理算法，并从理论上证明了算法的正确性。
The paper also proposes a fast algorithm to compute the median of a hierarchy coding data set, and gives a clear proof of the algorithm.
提出了一种基于均匀网格的自适应密度快速聚类新算法。
A fast clustering algorithm with adaptive density based on homogeneous grid is proposed.
设计了基于加权快速聚类的异常数据挖掘算法，以便能快速发现异常数据。
This article promoted outlier data mining algorithms based on weighted fast clustering to inspect and deal with outlier data effectively.
算法提出了一种简洁快速的初始聚类中心的选取规则，从而使获得的聚类结果为全局最优。
The new algorithm can obtain global optimal solutions through a new simple and efficient select rule of the initial cluster centers.
分别采用模糊c -均值聚类方法和快速全局C -均值聚类两种算法实现化工建模所需训练数据的有效提取。
In order to getting the effective training data of chemical engineering modeling, two algorithms that fuzzy C-means and fast global fuzzy C-means clustering were used.
DBSCAN是一种基于密度的空间聚类算法，在处理空间数据时具有快速、有效处理噪声点和发现任意形状的聚类等优点。
DBSCAN is a density based clustering algorithm that can efficiently discover clusters of arbitrary shape and can effectively handle noise.
提出了一种在格的拓扑序列上进行概念聚类的快速算法，并且定义了概念聚类间基于偏序的层次关系。
Next, a fast fuzzy conceptual clustering algorithm is proposed to cluster the fuzzy concept lattice into conceptual clusters.
接下来，快速模糊概念聚类算法，提出集群的模糊概念格为概念集群。
Next, a fast fuzzy conceptual clustering algorithm is proposed to cluster the fuzzy concept lattice into conceptual clusters.
经实践证明，该算法能快速、有效地对样本进行聚类，且特别适用于含有噪声样本的环境。
It's proved that this algorithm can cluster the samples fast and efficiently, and adapts to the environment …
目前多数聚类算法不能很好地适应文本聚类的快速自适应需求。
Most clustering algorithms can not meet the demand of speed and self-adapting about text clustering.
实验结果表明，该算法能快速、有效地识别任意形状、不同大小和密度聚类的边界点。
Experimental results show that the algorithm can identify boundary points in noisy datasets containing clustering of different shapes and sizes effectively and efficiently.
该算法通过闭合运算，将空间对象聚成类，一次完成三维空间聚类，可以快速处理非凸的、复杂的聚类形状。
This algorithm could not only complete 3d spatial clustering at a time, and process clustering in-convex and complicated objects rapidly.
该算法通过闭合运算，将空间对象聚成类，一次完成三维空间聚类，可以快速处理非凸的、复杂的聚类形状。
This algorithm could not only complete 3d spatial clustering at a time, and process clustering in-convex and complicated objects rapidly.
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