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.
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.
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.
空间聚类是空间分析和空间数据挖掘的重要方法和研究内容。
Spatial clustering analysis is important method and study content of spatial analysis and spatial 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。
This paper proposes a rough spectral clustering algorithm and apply the algorithm on text data mining.
聚类是数据挖掘中重要的研究课题。
实验结果表明，改进的KFCM算法对软件工程数据的挖掘有很好的聚类效果，且有较高的效率。
Finally, the experimental results illustrate the improved KFCM algorithm can achieve good clustering performance and high efficiency for software engineering data mining.
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.
本文介绍了数据挖掘理论，对聚类及孤立点检测算法进行了深入地分析研究。
In this thesis, the author presents the theory of data mining, and deeply analyzes the algorithms of clustering and outliers detection.
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.
聚类及孤立点检测算法研究已经成为数据挖掘研究领域中非常活跃的一个研究课题。
Research on clustering analysis and outlier detection algorithms has become a highly active topic in the data mining research.
本文分析了数据挖掘中的聚类技术以及聚类技术在客户细分领域中的研究现状。
This paper analyses the clustering technology in data mining and its current research status in customer segmentation.
数据挖掘的任务有关联分析、时序模式、聚类、分类与预测等。
The tasks of data mining include association rules analysis, time series module, cluster analysis, classification and predication and so on.
与传统的数据挖掘方法相比较，区间值聚类的数据挖掘模型更加高效、准确、符合实际。
By comparison with the traditional method for data mining, this method is more effective, more accurate, and more accordant to practice.
灰色数据挖掘模型在物流企业的管理决策问题中的应用证明了基于灰色系统理论的灰色预测和聚类模型是有效的、具有实用价值的数据挖掘模型。
The application of grey data mining model in the management and decision of logistics enterprises has proved that the grey forecasting model and clustering model is effective and of practical value.
本文介绍了数据挖掘理论，对网格聚类算法进行了深入地分析研究。
In this thesis, the author presents the theory of data mining, and deeply analyzes the algorithms of grid clustering.
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.
网格聚类算法研究已经成为数据挖掘研究领域中非常活跃的一个研究课题。
Research on grid clustering algorithms has become a highly active topic in the data mining research.
聚类作为数据挖掘的一个问题已经受到了数据库团体的密切关注。
Clustering is a data mining problem that has received significant attention by the database community.
数据聚类在数据挖掘、模式识别、图像处理和数据压缩等领域有着广泛的应用。
Clustering is a promising application technique for many fields including data mining, pattern recognition, image processing, compression and other business applications.
不但保留了系统聚类法中聚类过程的优点，而且还能挖掘出隐藏在原始数据中的有用信息。
It can not only remain the advantage of cluster procedures within the system cluster method but also can mine useful information hidden in the original data.
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.
聚类分析是数据挖掘领域中一个非常活跃的研究课题，应用于各个领域的聚类算法非常多。
Clustering is an active study subject in Data Mining. There are many algorithms of clustering that were applied in every field.
聚类是多媒体数据挖掘的重要任务之一，数据之间的相似性度量是聚类的基础和前提。
Clustering is one of the focused problems in multimedia data mining, and similarity measurement among data is fundamental to clustering.
数据挖掘是本课题的研究核心，主要包括关联规则发现、数据聚类和数据分类。
Data mining is the core topic of this paper. Basically, it includes associate rule founding, data clustering and data assorting.
聚类是数据挖掘领域中的一个重要分支，正逐步运用在商业、地质勘探、图像处理等领域。
Clustering in the field of data mining is an important branch is being used in commercial, geological exploration, image processing, and other fields.
本文介绍了数据挖掘的基本概念，说明了聚类是数据挖掘的一个很重要的功能。
Introduces the basic conception of Data Mining and explains the hierachical cluster is a main function of Data Mining.
本文介绍了数据挖掘的基本概念，说明了聚类是数据挖掘的一个很重要的功能。
Introduces the basic conception of Data Mining and explains the hierachical cluster is a main function of Data Mining.
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