Finally, this article discussed the first data-mining model, the regression model (specifically, the linear regression multi-variable model), and showed how to use it in WEKA.
最后，本文探讨了第一个数据挖掘模型：回归模型(特别是线性回归多变量模型)，另外还展示了如何在WEKA中使用它。
Since there are so many techniques with data mining, the major step to creating a good model is to determine what type of technique to use.
由于现在已经有很多数据挖掘技术，因此创建一个好的模型的最主要的步骤是决定要使用哪种技术。
A mining model describes the data that you will use, as well as other input parameters necessary for the model to run.
挖掘模型描述了您将使用的数据，还有其他为运行模型所必需的输入参数。
The ultimate goal of data mining is to create a model, a model that can improve the way you read and interpret your existing data and your future data.
数据挖掘的最终目标就是要创建一个模型，这个模型可改进您解读现有数据和将来数据的方式。
The new data has no classification (in this case, no checks on heart disease have been made) and the scoring process assigns a prediction to each new record according to the mining model.
新的数据没有分类别（这里是指还没有做过心脏病检查），评价过程根据挖掘模型将一个预测赋给每个新的记录。
That takes us to an important point that I wanted to secretly and slyly get across to everyone: Sometimes applying a data mining algorithm to your data will produce a bad model.
这也是我想审慎地告诉大家的一点：有时候，将数据挖掘算法应用到数据集有可能会生成一个糟糕的模型。
It's a bad data mining model for this data.
对于这类数据，分类树是一种极不适合的数据挖掘模型。
This fact leads data-mining mechanisms to imply semantics to the data being processed to define a useful data model.
这一事实导致数据挖掘机制对正在被处理的数据隐含了一些语义，以便定义一个有用的数据模型。
It's much easier to run the data mining model than to load the data into it.
运行数据挖掘模型要比将数据载入模型简单得多。
The resulting XML data mining model is stored in the database and can be accessed through SQL/XQuery.
这样得到的XML数据挖掘模型存储在数据库中，可以通过 SQL/XQuery进行访问。
Score new data using the mining model
使用挖掘模型对新数据进行评价
Thus, if the data warehouse is used for data mining, a low level of detailed data should be included in the model.
因此，如果数据仓库是用于数据挖掘的，就应该在模型中包含较低细节级(levelof detail)的数据。
Part 3 will bring the "Data mining with WEKA" series to a close by finishing up our discussion of models with the nearest-neighbor model.
第3部分是“用WEKA进行数据挖掘”系列的结束篇，会以最近邻模型结束我们对模型的讨论。
Putting forward a Data Mining model and some current problems.
提出了数据挖掘逻辑模型及存在的一些问题。
Design mode includes a graphical design surface used for selecting a single data mining model and input table, and a grid used for specifying the prediction query.
设计模式包含用于选择单个数据挖掘模型和输入表的图形设计图面，同时还包含用于指定预测查询的网格。
SAS system can totally meet the demands of this model with its perfect data management functions, strong data analysis ability and data mining ability.
SAS系统以其完善的数据管理功能、强大的数据分析能力和数据挖掘能力完全能够满足该模型的需要。
This release adds support for new SQL Server 2008 data mining features including holdout and cross-validation, a new Document Model wizard, and improvements to existing wizards.
此版本添加了新的SQLServer 2008数据挖掘功能支持，包括维持和交叉验证、新的文档模型向导和对现有向导的改进。
We propose a data mining model based on clustering and rough set.
提出了一种基于聚类和粗糙集的数据挖掘模型。
In the final chapter, we mine stock trading data using time series method, find out the model and outliers in the data and, at last, we show the more exact forecasting model and outlier mining method.
第五章利用时间序列的方法对证券交易数据进行了挖掘，找出了数据中的模式和异常，相对传统方法而言，给出了更精确的预测模型和异常挖掘方法。
Finally, we implement the key technique in the model using data mining.
最后，运用数据挖掘技术对模型中的关键技术加以实现。
New data mining algorithms can easily be integrated into this platform if they comply with the data model interface and mining model interface of this platform.
只要遵循该平台的数据模型接口和挖掘模型接口，新的数据挖掘算法可以很容易地集成到该平台中去。
Complying with our data object interface and mining model interface, new mining algorithms can be easily integrated to our system.
只要遵循我们的数据模型接口和挖掘模型接口，新的功能、算法可以很容易地集成到系统中来。
The comprehensive integrated DSS, which composed of data warehouse and OLAP, data mining, model base and knowledge base system, is a more sophisticated form of DSS.
将数据仓库、OLAP、数据挖掘、模型库、知识库系统结合起来形成的综合集成决策支持系统是更高形式的决策支持系统。
This paper introduces data mining concept, function and operation flow, and put forward using data mining technique to build individuation service model in physical education distance education.
本文介绍了数据挖掘技术的概念、功能及其运作流程，并提出了在体育远程教育中使用数据挖掘技术建立个性化服务的模型。
In the end, a complete model of data mining is proposed.
最后给出了一个完整的数据挖掘模型。
The DM and OLAP are the critical techniques of data analysis, the multi dimensional data mining model combining both techniques can enhance the performance and effects of data analysis.
DM技术与OLAP技术是电力营销决策支持系统中的关键数据分析技术，二者有机结合构成的多维数据挖掘模型能提高数据分析的效果和性能。
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。
This article introduces grey theory into logistics trade, and builds data mining model based on grey theory to solve the practical problem in logistics enterprises' management and decision.
本文提出灰色系统的理论与方法在物流行业的应用，针对物流企业管理决策的实际问题建立基于灰色系统理论的数据挖掘模型。
The Customer relationship management system based on data mining technique consists of data storage and colligation, multidimensional date analysis, data mining, model storage, method storage.
基于数据挖掘技术的客户关系管理系统由数据存储和综合、多维数据分析、数据挖掘、模型库、方法库组成。
After studying the analysis and comparison of the realization techniques of spatial database system and spatial data mining systems, we propose a development model of spatial data mining system.
在对空间数据库系统实现技术及空间数据挖掘系统等进行比较分析的基础上，提出了一种空间数据挖掘系统的实现模式。
应用推荐