用户属性数据集uads ？
Every attribute in the data set should be normalized, whereby each value is divided by the difference between the high value and the low value in the data set for that attribute.
数据集内的每个属性都应该是规格化的，因此，每个值均除以该属性在数据集内的最高值与最低值间的差值。
One defining benefit of clustering over classification is that every attribute in the data set will be used to analyze the data.
与分类相比，群集的一个好处是数据集内的每个属性都被用来分析该数据。
Running queries on these (instance data) views is a good choice if the business relevant attribute is not set for a business process.
如果没有为业务流程设置与该流程相关的属性，那么在这些(实例数据)视图上运行查询操作是一个很好的选择。
For a Data Table control, the Binds to multi-valued option would be selected, and Attribute for repeating values would be set to var.
对于一个DataTable控件，绑定至多个值选项将会被选中，而重复值属性将会设置为var。
Attribute validators verify that a single attribute is set to a valid value, length, format, or data type.
Attribute确认器会证实单个的属性会设置为一个有效的值，长度，格式，或者数据类型。
There is no label beside the data entry field, but it has a long description, so the title attribute should be set for this form.
数据输入字段旁没有标签，但有一段长描述，因此应当为此表单设置title属性。
The data can then be set in the container, as specified by the boundTo attribute in the source portlet specification.
在容器中可设置数据作为在源portlet规范中boundto属性的指定值。
However, if the Ignore missing data attribute of a business process is set, these exceptions are suppressed.
不过，如果设置了业务流程的Ignoremissingdata属性，则会禁止这些异常。
In this example, the data is set in the RequestScope as a request attribute in the action code that is invoked.
在本例中，数据在RequestScope中设置为调用动作代码的请求属性。
For example, if the mapping attribute of the DataSource object is not set, the data source might not act as the data source in the container-managed persistence (CMP).
例如，如果未设置DataSource对象的mapping属性，则数据源的行为方式不会与容器管理的持久性(CMP)中的数据源相同。
You can set the other data types to be non-mandatory by adding the nillable="true" attribute to that element in the schema.
可以通过为模式中的该元素添加nillable="true"属性来将其他数据类型设置为非必填项。
A model of data mining is set up after preparation of data by means of attribute structure, and association rule algorithms are carried out. the data mining result is explained and analysed.
采用了属性构造法进行数据预处理，建立了数据挖掘模型，实现了关联规则算法，并对挖掘结果进行解释与分析。
The Rough Set Theory can handle such problems as data reduction, data mining, the evaluation of attribute importance, the formation of decision algorithm etc.
利用粗集理论处理的主要问题包括：数据简化、数据相关性的发现、数据意义的评估、由数据产生决策算法等。
Four kinds of condition entropy are defined in this paper. Accordingly, four kinds of entropy based methods for the attribute reduction in the rough set data analysis are proposed.
本文定义了四种条件熵，并在此基础上提出了四种基于熵的方法，以用于粗糙集数据分析中的属性简约。
The ascertainment of the core attribute set is the important process for discovering decision rules from mass data.
核属性集的确定是从海量数据中发现决策规则的重要步骤。
The authors can find attribute set of churn customer's basis character and the conclusion that whether or not churn by implementing neural network forecast on the basis data of customer.
通过对客户的基本数据进行神经网络预测，可以发现描述流失客户基本特征的属性值集合以及对应的是否流失的结论。
Then, based on the different priorities of roles, patients set purpose attribute of the privacy data, making personal policies.
患者根据个人隐私侧重点不同，为隐私数据设置目的属性，制定个人策略。
The main contents include the approximation set, decision systems, data preprocessing and attribute reduction and so on. It is a effective method of dealing with incomplete, inaccurate data.
其主要内容包括近似集、决策系统、数据预处理以及属性约简等等，是一种处理不完整，不精确数据的有效方法。
With regard to the attribute values in decision table, which are described with hybrid data, a new algorithm of attribute reduction based on rough set theory is proposed.
针对决策表中属性取值为杂合数据的情况，提出了基于粗糙集理论的属性约简算法。
The data were classified, transformed and saved in the form of database. Soil attribute database, administrative code database and parameter database for prescription fertilization were set up.
本系统首先将土壤普查数据以数据库的形式加以保存，分别建立了土壤属性数据库、行政编码库和配方施肥参数库三个数据库。
Attribute reduction, a basic conception in rough set theory, is introduced at first, then applied to forestry information management. The ability of data analysis is enhanced by this way.
本文介绍了粗糙集理论基本内容属性约简，并将其应用在林业信息管理中，通过实例说明提高数据分析能力的方法。
This paper mainly discusses topics on solving attribute reduction problems by applying rough set methods in the field of scientific data mining.
本论文主要讲述数据挖掘中采用粗糙集方法实现数据预处理中冗余属性约简的问题。
To use of rough set theory for data mining and the extraction rules of the knowledge, the most important point is that based on the attribute reduction and rule extraction algorithms of rough set.
利用粗糙集理论进行数据挖掘，抽取知识规则，最重要的一点就是基于粗糙集的属性约简和规则提取算法的研究。
The experimental results indicate that the algorithm is very successful in picking different types of attacks in data set based on different attribute features.
实验表明该算法针对不同特征属性的数据集，检测不同类型的入侵行为，具有很好的检测结果。
The experimental results indicate that the algorithm is very successful in picking different types of attacks in data set based on different attribute features.
实验表明该算法针对不同特征属性的数据集，检测不同类型的入侵行为，具有很好的检测结果。
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