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.
最后，使用增量式的概念格生成算法对搜索结果片段进行概念聚类，并从中产生每个聚类的主题。
Finally, incremental algorithm of producing concept lattice is used to carry on concept clustering to the passage of search results, and produced the theme of each cluster result from it.
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.
通过概念聚类识别孤立点，运用规划识别技术和贝叶斯因果网络实现目标的预测、识别，最终实现系统自学习。
The system applies conceptual clustering technology to recognize outliers, and uses plan recognition and causal network to predict and recognize the target.
根据构建概念格过程中概念聚类的特性，本文引出了概念格图形的近似自相似性这一特征作为信息分类的评估系数。
Based on the characteristics of conceptual cluster during the construction of concept lattices, this paper gave a characteristic of approximate self-similarity to value the information classification.
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.
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.
通过对模糊c均值算法聚类特性的分析，引入了约束函数及模式相似度的概念，提出了改进的FCM算法。
With the clustering feature analyzed, restrained function and pattern similarity are introduced. Then the algorithm of improved FCM is presented.
针对词、潜在概念、文本和主题之间的模糊关系，提出一种基于信息论的潜在概念获取与文本聚类方法。
To emphasize the fuzzy relation among words, latent concepts, text and topics, an information theory based approach to latent concept extraction and text clustering is proposed.
通过对离群数据来源及特性进行分析，定义了离群贡献度的概念，提出了一种基于特征赋权的离群数据再聚类算法。
By analyzing the origin and feature of outliers, a concept of exceptional contribution degree is defined and then an algorithm for re-clustering outliers based on feature weighting is proposed.
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.
在属性测度概念的基础上，运用属性聚类网络方法解决模式识别问题。
Based on concepts of attribute measurement, we used attribute clustering network approach to resolve some problems of pattern recognition.
这里的问题在于，这个类从概念上来说不再是内聚的，从而导致了将来可能有很多理由会去修改它。
The issue with this is that your class won't be conceptually cohesive and it will give it many reasons to change.
在分析核方法的核心概念基础上，提出了一种基于核方法的聚类算法。
Based on the analysis of the core concepts of the kernel methods, a clustering algorithm based on kernel methods was put forward.
本文借助于两个样品之间的差异度和有序样品的差异序列两个概念，提出了有序样品聚类的差异序列法法。
In this paper, with the aid of the two concepts of diversity between two samples and diversity sequence of ordered samples, diversity sequence method is presented for clustering ordered samples.
基于形式概念分析和概念相似度，给出一种新的多背景文本模糊聚类方法和模型。
A novel multi-context text fuzzy clustering method and its model based on formal concept analysis and concept similarity is proposed.
面对满足二维空间邻接条件的聚类问题，定义了邻接矩阵的概念。
In order to dealing with the clustering considering the condition of planar adjacency relationship, the concept adjacency matrix is defined.
借助于任意两个样品之间的差异度和有序样品的全差异矩阵的概念，提出有序样品聚类的全差异矩阵法。
With the aid of the two concepts of diversity between any two samp le sand total diversity martrix of orderd samples, diversity matrix method is presented for clustering orderd samples.
为了改善文本聚类的准确度，提出用基于主题概念子空间的模糊c -均值聚类(TCS2FCM)方法来分类文本。
To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space (TCS2FCM) is introduced for classifying texts.
针对高属性维稀疏数据聚类问题，提出高属性维稀疏信息系统概念，给出一种新的基于稀疏特征差异度的动态抽象聚类方法。
The concepts of high attribute dimensional information system are firstly proposed, and a new dynamic clustering method on the basis of sparse feature difference degree is presented.
考虑到它们的结果不一定全部都是好的，因此提出了一个信任度系数的概念，根据系数值从中选择较优的那些聚类成员进行融合。
Considering that their results are not all good, the concept of a confidence factor is proposed. According to the value of factor, we combine the clustering members that are better.
本文首先介绍了增量聚类算法的分类以及研究现状，提出了增量聚类算法等价性概念；
This paper firstly introduces the classification of incremental clustering algorithms and the research state, defines the concept of algorithm equivalence.
利用知网较完备的知识体系来构造概念词典和概念层次结构，实现了一种以知网为背景知识的基于概念的中文文本聚类算法。
Using HowNet's complete knowledge system to construct Concept Dictionary and Concept Hierarchy, we realized a kind of Chinese text clustering algorithm based on concept.
其次，采用机器学习技术，包括文本分类、聚类，文本概念抽取，从概念层次理解文本信息；
Secondly, the system can distinguish the domain of the web page and understand the document at the concept level by text classification, clustering and concept extraction based machine learning.
提出了一种以语言概念空间中的概念为聚类对象的信息检索方法以及适合于该方法的聚类算法。
An information retrieval model based on language concept space and a clustering method which serves the IR model is propsed.
该方法利用了概念格对信息聚类的特性，突破了传统方法相关度计算方法的设计思路，拓宽了概念格的应用范围。
This method breaks through the traditional design ideas, for the usage of information clustering of concept lattice Characteristics. And it also broadens the application of concept lattices.
然后，研究了如何用概念描述和概念对比的数据挖掘方法描述和评估客户细分，这一工作是对在数据挖掘模块中使用聚类算法进行客户细分的完善和补充。
The emphasis of research is that how to describe and evaluate the refinement result of customer by two data mining methods - concept description and concept parallel.
TCU SS算法利用两个概念列表中单词间的语义相似度作为文档间相近程度的度量，并以图为基础进行聚类分析，避免有些聚类算法对聚簇形状的限制。
TCUSS algorithm measures the document similarity by semantic similarity of concepts in concept lists, then clusters the document based on graph analysis, thus avoiding the restrict of clusters shape.
TCU SS算法利用两个概念列表中单词间的语义相似度作为文档间相近程度的度量，并以图为基础进行聚类分析，避免有些聚类算法对聚簇形状的限制。
TCUSS algorithm measures the document similarity by semantic similarity of concepts in concept lists, then clusters the document based on graph analysis, thus avoiding the restrict of clusters shape.
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