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.
基于一种用于混合属性数据的距离定义和改进的最近邻分类方法，提出了一种基于聚类的有指导的入侵检测方法。
A clustering-based and supervised intrusion detection method was proposed with new distance definition for mixed-attribute data and improved nearest neighbor classification method.
基于聚类的入侵检测方法大都是以距离差异为基础的，而同等重要地依赖所有属性的相似性度量会引起误导。
Intrusion detection methods based clustering are based on distance difference, but to depend on the similarity measurement of all attributes in the same degree tend to arouse misleading.
文中提出了一种基于DBSCAN的算法，可以处理非空间属性，同时又可以加快聚类的速度。
Proposes an improved DBSCAN algorithm which can handle non-spatial properties and greatly accelerate the speed of clustering.
通过理论分析，属性均值聚类是比模糊均值聚类更稳健的聚类方法。
Attribute means clustering is more robust than fuzzy means clustering by theoretical analysis and numerical example.
本文研究了基于遗传算法和社会演化算法的数据挖掘和文本挖掘方法，主要包括数据挖掘和文本挖掘中的属性约简问题、聚类问题。
Several methods of data mining and text mining have been studied in this paper, which mainly includes: attribute reduction methods, clustering methods.
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.
运用分类标准矩阵、单指标白化权函数和置信度原则，提出了基于属性识别的灰色聚类方法。
Based on classify criterion matrix, single-valued whitenization weight function and reliability code, grey attribute recognition clustering method is put forward.
通过用已知的统计属性模拟模块网络，介绍并比较了特别为聚类和网络分析目的的筛选方法。
Filtering methods intended specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties.
改进后的聚类结果既消除了采样误差，又保持了云类样本的基本特征属性。
Therefore, the improved FCM clustering results can reduce the sampling errors and retain the main attributes of cloud classification samples.
给出一种新的相似模式聚类算法，能高效地得到访问者对象在整个或者部分属性空间的相似访问行为模式。
The paper proposes a novel similar pattern clustering algorithm that can discover the pattern that exhibits a coherent pattern on a subset of dimensions.
应用聚类方法研究了数量关联规则提取过程中的连续属性离散化问题。
This paper presents a cluster method for discretization in the processing of mining quantitative association rules.
聚类搜索的目的就是为了快速帮助用户寻找信息，它的突出特点是根据某一属性，对搜索返回的结果进行聚类。
Clustering search's purpose is to help users find information quickly, it's outstanding feature is based on a property, on the search results returned by the cluster.
针对电信客户的有效细分问题，利用属性相似度度量思想，提出了一种面向复杂属性的聚类算法。
In order to divide the telecom customers effectively, a new clustering algorithm for complex attributes was proposed based on feature similarity measurement idea in this paper.
提出CF-WFCM算法，该算法分为属性权重学习算法和聚类算法两部分。
This paper proposes CF-WFCM algorithm including feature weight learning algorithm and clustering algorithm.
现有的数据流聚类算法无法处理高维混合属性的数据流。
Existed data stream clustering algorithms can not deal with the data stream with high-dimensional heterogeneous attributes.
由于原始海量数据规模较大，聚类算法难以实现，而且聚类分析有时候只考虑关键属性作为分类参数。
The scale of original data is very large. It is difficult to realize the clustering algorithm. Clustering analysis often takes the key attributes as classification parameters.
传统聚类算法仅考虑属性相似性，较少利用对象间的相互关系。
Traditional clustering method for attribute space ignores the object relationship information.
聚类分析方法利用距离系数的概念，把相关性较大的属性参数聚咸一类，使参数有一个正确的全面的分类。
Cluster analysis using the conception of distance parameter, made the attributes that have the high correlativity become the same sort. So the classification of attributes become complete and correct.
通过聚类，人们能够识别密集的和稀疏的区域，因而发现全局的分布模式，以及数据属性之间有趣的相互关系。
By clustering, one can identity dense and sparse regions, therefore, discover overall distribution patterns and interesting correlations among data attributes.
进而将聚类得到的属性隶属矩阵用于属性约简，并提出一种基于信息熵的模糊粗糙集知识获取的方法。
Then the membership matrix obtained by clustering algorithm was used to reduce attribute set. Finally, based on entropy, a knowledge acquisition method of fuzzy Rough Set (RS) was put forward.
通过对抽取模式进行聚类并按内涵属性类型划分为不同的簇，再按照不同的簇从词典中抽取出不同内涵属性类型的内涵属性值。
By clustering extraction patterns are divided into different clusters and then according to different clusters the different attribute values are extracted from the dictionary.
但其缺点是不能处理混合属性数据，聚类结果对初值有明显的依赖性。
The problem is that both of the two algorithms cannot deal with mixed valued data, and clustering results significantly depend on the initial value.
但其缺点是不能处理混合属性数据，聚类结果对初值有明显的依赖性。
The problem is that both of the two algorithms cannot deal with mixed valued data, and clustering results significantly depend on the initial value.
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