一个域包是一个互相协作以支持可以被认为是黑盒的内聚契约集的类集。
A domain package is a set of classes that collaborate among themselves to support a cohesive set of contracts that can be considered black boxes.
提出了一种基于聚类和粗糙集的数据挖掘模型。
We propose a data mining model based on clustering and rough set.
介绍模糊数学的基本概念，建立了用于缺陷模糊模式识别的两种数学模型，即模糊集法与模糊聚类法。
The concept of fuzzy mathematics is described and two fuzzy pattern recognition models of flaws based on fuzzy set method and fuzzy cluster method have been established.
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.
聚类不需要训练集，但准确率较低。
The training set is not needed in clustering but the accuracy is lower.
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.
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.
借助模糊聚类技术和粗糙集理论提出了一个基于客观信息熵的多因素权重分配方法。
Using fuzzy aggregation theory and rough set theory, this article puts out a weight allocation method based on impersonal message entropy.
该方法很好的结合了模糊聚类法和粗糙集理论，对知识的模糊性以及相关信息获取及处理的弊端都进行了修正。
The method revises the knowledge fuzzy and the abuse of informationaccessing and processing by combining the theory of rough sets with fuzzy clustering approach.
数据集的聚类结果是否合理的问题属于聚类有效性问题。
The reasonableness of clustering result is belongs to cluster validity problem.
提出了应用模糊聚类划分储集层岩石物理相的方法。
A method for division of reservoir petrophysical facies by fuzzy clustering is proposed.
模糊聚类、识别与优选是系统优化模糊集分析理论的数学基础。
The theory of fuzzy cluster, recognition and optimum decision is the mathematical basis of optimum fuzzy set of system.
此方法首先在特征空间中对支持向量进行聚类，然后寻找特征空间中的聚类中心在输入空间中的原像以形成约简向量集。
The method firstly organizes support vectors in clusters in feature space, and then, it finds the pre-images of the cluster centroids in feature spa.
该文给出了一种基于模糊聚类的粗糙集决策表分析方法。
In this paper, a rough set decision table analysis method based on fuzzy cluster is presented.
结合模糊聚类和粗糙集提出了一种基于精简的模糊规则库分类算法。
Proposes a classification algorithm based on simplified fuzzy rules base combining fuzzy clustering with rough set.
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；
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.
BIRCH算法是针对大规模数据集的聚类算法。
BIRCH algorithm is a clustering algorithm for very large datasets.
在文本分类方面，本文提出了一种基于聚类和粗糙集理论相结合的文本自动分类方法。
In text classification, we advance an automatic text classification way based on clustering and Rough Set Theory.
为了提高模糊支持向量机在数据集上的训练效率，提出一种改进的基于密度聚类(DBSCAN)的模糊支持向量机算法。
In order to improve the training efficiency, an advanced Fuzzy Support Vector Machine (FSVM) algorithm based on the density clustering (DBSCAN) is proposed.
根据粗糙集理论的边界区域和V -支持向量机的优点对支持向量聚类算法进行改进。
According to the border region of rough set theory and the merits of V-support vector machine, the algorithm of support vector clustering is improved.
本文定义了聚类中心以及点到聚类中心的距离，提出了一种平面点集的动态聚类分析方法。
In this paper, cluster centre and the distance from point to the cluster centre was defined, and a dynamic analysis method for plane point set was put forword.
本文提出了一只基于免疫进化的模糊更新算法，算法能够较好的体现环境的变化，使得更新后的模糊集更有利于模糊聚类的需要。
This paper proposed an algorithm based on immune evolution, the algorithm reflect the changing of the environment and satisfied the need of the fuzzy clustering.
通过研究核聚类算法，以及粗糙集，提出了一个新的用于聚类分析的粗糙核聚类方法。
By means of analyzing kernel clustering algorithm and rough set theory, a novel clustering algorithm, rough kernel k-means clustering algorithm, was proposed for clustering analysis.
现有的半监督聚类方法较少利用数据集空间结构信息，限制了聚类算法的性能。
Most of the existing semi-supervised clustering methods neglect the structural information of the data, while the few constraints available may degrade the performance of the algorithms.
本文在粗糙集理论的背景知识下，对于文本的粗糙集表承模型和基于此模型下的聚类在信息过滤系统中的应用，进行了深入的研究。
With the rough set theory as background, this paper has studied deeply the rough set representation model and the clustering based on this model.
提供了用来剖析复杂数据集的聚类、机器学习和分类的很多内置方法。
Many built-in methods for clustering, machine learning and classification are provided for dissecting complex datasets.
模糊聚类划分方法能较好地反映储层储集性能的成因特征。
The new method can reflect the genetic features of reservoir.
将该种模型运用于公开的白血病基因表达数据集进行实验，实验表明该方法能自动获取基因表达数据的聚类数，并得到较高的分类准确率。
We applied the model to analyze the expression data set of leukaemia. The experimental result proved that this model can get cluster Numbers automatically and a high accuracy of classification.
将该种模型运用于公开的白血病基因表达数据集进行实验，实验表明该方法能自动获取基因表达数据的聚类数，并得到较高的分类准确率。
We applied the model to analyze the expression data set of leukaemia. The experimental result proved that this model can get cluster Numbers automatically and a high accuracy of classification.
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