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.
理论分析和实验结果表明，该方法具有良好的聚类质量、较小的内存开销和快速的数据处理能力。
Theoretical analysis and comprehensive experimental results demonstrate that the proposed method is of high quality, little memory and fast processing rate.
这种检索方法在文本聚类的基础上，基于概念空间并与传统的关键词检索相结合能够帮助用户快速、准确地定位所需要查找的信息。
Based on concept space and text clustering technique as well as traditional keyword searching method, it could help users to locate the information they need quickly and precisely.
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.
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.
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.
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.
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.
基于数学形态学的方法，研究了两种针对这种较复杂情况的成熟草莓果实分割的方法，即聚类快速分割法和分水岭区域分割法。
Based on the mathematical morphological algorithm, two methods to solve this complexity were proposed, namely, Clustering Fast Segmentation and Watershed Region Segmentation.
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.
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.
聚类搜索的目的就是为了快速帮助用户寻找信息，它的突出特点是根据某一属性，对搜索返回的结果进行聚类。
Clustering search's purpose is to help users find information quickly, it's outstanding feature is based on a property, on the search results returned by the cluster.
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.
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.
因此，对检索结果进行聚类处理成为提高用户查找速度和快速定位所需信息的一个有效解决方案。
Accordingly, clustering the search results is one efficient solution to improve the lookup speed and fast locate the required information.
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.
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.
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.
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.
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