面对大规模的、高维的数据，如何建立有效、可扩展的的聚类数据挖掘算法是数据挖掘领域的一个研究热点。
Facing the massive volume and high dimensional data how to build effective and scalable clustering algorithm for data mining is one of research directions of data mining.
接着，讨论了代理商的信任度计算问题，并从聚类数据挖掘方面对代理商的信任度计算进行了研究和验证，给出了实验结论。
Then the calculation of agent trust degree is discussed, and studied and validated in terms of clustering data mining, and then I make an experimental conclusion.
这种算法利用了数据挖掘中的聚类技术，可用于常规雷达和特殊雷达的信号分选。
The algorithm makes use of the clustering technology of data mining, can apply to general radar and special radar.
有效的离散化可以显著地提高系统对样本的聚类能力，增强系统对数据噪音的鲁棒性。
Effective data discretization can obviously improve system ability on clustering instances, and can also make systems more robust to data noise.
这里提出了一种高效的基于模糊c均值(FCM)聚类的彩色图像分割方法，它利用塔形数据结构对彩色图像进行多层分割。
An efficient segmentation method based upon fuzzy c-means (FCM) clustering principles is proposed. The approach utilizes a pyramid data structure for the hierarchical ana - lysis of color images.
该算法将具有足够高密度的区域划分为簇，并可以在带有“噪声”的空间数据库中发现任意形状的聚类。
It can handle spatial data and spot any-shape clusters in a noised spatial database by dividing them into clusters with high enough density.
本文提出了一种有效的支持海量图像数据库Q BE查询的聚类索引算法。
This paper proposes an indexing algorithm of clustering which supports QBE image retrieval for large image databases.
聚类是一种把整个数据库分成不同的群组，使群与群之间差别很明显，而同一个群之间的数据尽量相似的算法。
Cluster is an algorithm, which can divide the data in the database into different groups, and there are obvious distinctions among groups.
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。
The new clustering algorithm is analyzed on several aspects and tested on the published yeast cell-cycle microarray data.
聚类分析是数据挖掘的一个重要研究方向，而PAM算法是聚类算法中一个重要的方法。
Cluster is an important research direction and the PAM algorithm is one of the most important method.
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.
针对异类传感器观测空间不一致的问题，提出了基于模糊聚类的异类多传感器数据关联算法。
For the inconsistency problem of heterogeneous sensors' measurement Spaces, a new data association (da) algorithm based on fuzzy clustering algorithm is presented.
然后对数据先进行聚类，再在聚类结果中发掘频繁项目集；
The second, clustered the data, and then discovered frequent items sets in the result of clustering.
一种新的抽样方法是把数据挖掘技术中的分类、聚类及离群点挖掘等应用到审计风险管理中去。
A new sampling method is proposed, which USES the latest technologies of database. It applies classification rule mining, clustering rule and outlier mining to the management of Audit Risk.
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.
聚类通过比较数据的相似性和差异性，能发现数据的内在特征及分布规律，从而获得对数据更深刻的理解与认识。
By contrasting the similarity and dissimilarity in data, clustering can find out the data's inner characteristic and distribution rule, so we can obtain the further understanding.
提出了一种基于密度网格的数据流聚类算法。
This paper introduced a density grid-based data stream clustering algorithm.
本文介绍了地学空间数据迭代聚类的算法原理。
This paper presents algorithmic principles for approaching clustering of geo-spatial data.
聚类是数据挖掘中重要的研究课题。
基于网格的多密度聚类算法不仅能够对数据集进行正确的聚类，同时还能有效的进行孤立点检测，有效的解决了传统多密度聚类算法中不能有效识别孤立点和噪声的缺陷。
GDD algorithm can not only clusters correctly but find outliers in the dataset, and it effectively solves the problem that traditional grid algorithms can cluster only or find outliers only.
聚类算法是数据挖掘算法中的重要解决方法。
Clustering algorithm is an important one in data mining methods.
在数据挖掘领域，聚类用于发现数据的分布模式和数据间的相互关系。
In data mining, clustering is used to discover groups and identify interesting distribution in the underlying data.
聚类是数据挖掘中的典型算法，其中的K -均值算法是最基本的算法，由该算法产生了许多经典而高效的算法。
Clustering algorithms are the typical algorithms in the data mining, the K-means algorithm is the most basic algorithm, which has produced many classics and highly effective algorithms.
结合文本数据的语义相似度，给出一种基于语义密度文本数据聚类的方法。
Combined with semantic similarity of text data, this paper gives a method of text data clustering based on semantic density.
此聚类算法可以在线地划分输入数据，逐点地更新聚类，自己组织模糊神经网络的结构。
This clustering algorithm can on-line partition the input data, pointwise update the clusters, and self-organize the fuzzy neural structure.
在许多聚类应用中，数据对象是具有高维、稀疏、二元的特征。
The data sets have features such as high-dimensional, sparseness and binary value in many clustering applications.
常用的数据挖掘方法包括描述、分类、关联规则、聚类、孤立点检测、模式匹配、数据可视化等。
Several major kinds of data mining methods, including characterization, classification, association rule, clustering, outlier detection, pattern matching, data visualization, and so on.
该方法的目的是实现多传感器观测数据的模糊聚类，使源于不同目标的观测数据能正确划分到该目标中去。
Its aim is to accomplish fuzzy cluster among data of multisensor in order to make out the affiliation between different data and different targets.
该方法的目的是实现多传感器观测数据的模糊聚类，使源于不同目标的观测数据能正确划分到该目标中去。
Its aim is to accomplish fuzzy cluster among data of multisensor in order to make out the affiliation between different data and different targets.
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