The paper establishes decision-making tree data mining model and clustering data mining model to make data mining with a multi-dimension data collection.
建立了渔船决策树挖掘模型及渔船年审聚集挖掘模型，对多维数据集进行了数据挖掘。
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.
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。
To integrate the results of clustering data mining and based on visual model requirement, network structure diagram of HQDS would be drawn, gene network structure be study with RT-PCR.
结合挖掘结果，运用网络结构可视化思想构建心气虚证网络结构图，并运用逆转录聚合酶链反应(RT - PCR)探索心气虚证基因网络结构。
Data mining commonly involves a few standard tasks that include clustering, classification, regression, and associated rule learning.
数据挖掘通常涉及到一些标准的任务，包括聚集、分类、回归分析和关联性规则学习。
Clustering denotes a data mining technique that groups data records into clusters of pair-wise similar records by their properties.
集群是一种数据挖掘技术，这种技术根据数据记录的属性将相近的数据记录指定到集群中。
However, for the average user, clustering can be the most useful data mining method you can use.
不过，对于一般的用户，群集有可能是最为有用的一种数据挖掘方法。
Table 1 lists and describes some typical types of data-mining clustering.
表1列出并描述了一些典型类型的数据挖掘聚集。
This article discussed two data mining algorithms: the classification tree and clustering.
本文讨论了两种数据挖掘算法：分类树和群集。
Future articles will touch upon other methods of mining data, including clustering, Nearest Neighbor, and classification trees.
本系列后续的文章将会涉及挖掘数据的其他方法，包括群集、最近的邻居以及分类树。
The algorithm makes use of the clustering technology of data mining, can apply to general radar and special radar.
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。
Clustering analysis is a major field in data mining, which is an important method of data partition.
聚类分析是数据挖掘中的一个重要研究领域，是一种数据划分或分组处理的重要手段和方法。
Clustering analysis is an important approach of data mining, and an important content of human activity.
聚类分析是数据挖掘的重要方法之一，也是人类活动的一个重要内容。
Clustering is an important topic in the data mining.
聚类是数据挖掘中重要的研究课题。
Customer clustering analysis in customer relation management (CRM) is a new study domain, and it is part of data mining.
客户关系管理(CRM)中的客户聚类分析是一个新的研究领域，属于数据挖掘的应用范畴。
Clustering is a data mining problem that has received significant attention by the database community.
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 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。
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 analytical means of Data Mining is combined with Intrusion Detection System and an intellectual structure pattern used in Intrusion Detection Syst.
将数据挖掘的聚类分析方法与入侵检测系统相结合，提出了一种入侵检测系统的智能结构模型。
Clustering is one of the focused problems in multimedia data mining, and similarity measurement among data is fundamental to clustering.
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。
Several major kinds of data mining methods, including characterization, classification, association rule, clustering, outlier detection, pattern matching, data visualization, and so on.
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。
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。
Classification and clustering are both commonly used data mining methods. The advantage of classification is that the accuracy is higher, but the labeled training set is needed.
分类和聚类都是常用的数据挖掘方法，分类的优点是准确率较高，但需要带有类别标注的训练集；
Finally, the experimental results illustrate the improved KFCM algorithm can achieve good clustering performance and high efficiency for software engineering data mining.
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。
Clustering is a promising application technique for many fields including data mining, pattern recognition, image processing, compression and other business applications.
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。
In this thesis, the author presents the theory of data mining, and deeply analyzes the algorithms of clustering and outliers detection.
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。
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.
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。
Data mining always faces complicated tasks that including classification, prediction, association rule discovering and clustering, etc.
数据挖掘面对的任务是复杂的，通常包括分类、预测、关联规则发现和聚类分析等。
Clustering analysis is one of the basic methods of the data mining and knowledge finding and it is a non - surveillance data classification method.
聚类分析是在无先验知识无指导下进行数据无监督分类的一种数据挖掘技术。
This article promoted outlier data mining algorithms based on weighted fast clustering to inspect and deal with outlier data effectively.
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.
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。
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