WEKA can be used as a stand-alone Java library which you can drop into your server-side environment and call its API like any other Java library .

WEKA还能被用作一个独立的Java库，您可以将其放入到您服务器端的环境内并像其他Java库那样调用它的API。

Creating the regression model with WEKA

用 WEKA创建一个回归模型

It 's Java-based so if you don 't have a JRE installed on your computer download the WEKA version that contains the JRE as well .

因为它基于Java，所以如果您在计算机上没有安装JRE，那么请下载一个包含JRE的 WEKA版本。

Study to web transactions clustering algorithm on WEKA

基于 WEKA 平台的Web事务聚类算法的研究

When we click Start this time WEKA will run this test data set through the model we already created and let us know how the model did .

当我们这次单击Start时， WEKA将会贯穿我们已经创建的这个模型运行测试数据集并会让我们知道模型的情况。

To take this simple example to the next level let 's take a look at a data file that the WEKA Web site supplies to us as a regression example .

要想把这个简单的示例提升到一个新的级别，让我们来看一下 WEKAWeb站点上作为回归示例提供给我们的一个数据文件。

As you can imagine the central building block in the WEKA API is going to be the data .

正如您所想， WEKAAPI内的这个中心构建块就是数据。

In the previous two articles in this Data mining with WEKA series I introduced the concept of data mining .

在这个“用 WEKA进行数据挖掘”系列之前的两篇文章中，我介绍了数据挖掘的概念。

So now we have the data loaded into WEKA .

现在我们已经将数据载入了 WEKA。

This tells WEKA that we want to build a regression model .

这会告诉 WEKA我们想要构建一个回归模型。

Load the data file bmw-training . arff ( see Download ) into WEKA using the same steps we 've used up to this point .

使用我们之前使用过的相同步骤来将数据文件bmw-training.arff（参见下载）载入 WEKA。

This article also introduced you to the free and open source software program WEKA .

本文还向您介绍了一种免费的开源软件程序 WEKA。

Yet the results we get from WEKA indicate that we were wrong .

然而，我们从 WEKA获得的结果表明我们错了。

The Research and Implementation of Text Clustering Based on WEKA

基于 WEKA 平台的文本聚类研究与实现

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。

To load data into WEKA we have to put it into a format that will be understood .

为了将数据加载到 WEKA，我们必须将数据放入一个我们能够理解的格式。

It also has a general API so you can embed WEKA like any other library in your own applications to such things as automated server-side data-mining tasks .

它还有一个通用API，所以您可以像嵌入其他的库一样将 WEKA嵌入到您自己的应用程序以完成诸如服务器端自动数据挖掘这样的任务。

Now that you 're familiar with how to install and start up WEKA let 's get into our first data-mining technique : regression .

在熟悉了如何安装和启动 WEKA 后，让我们来看看我们的第一个数据挖掘技术：回归。

In our previous articles we use WEKA as a stand-alone application .

在我们之前的文章中，我们将 WEKA 用作一种独立的应用程序。

The final section of the article showed that you shouldn 't be constrained to using WEKA with the Explorer window as a stand-alone application .

本文的最后一节显示了您不应该将自己限制于只使用 WEKA与Explorer窗口作为一个独立的应用程序。

In this view WEKA allows you to review the data you 're working with .

在这个视图中， WEKA允许您查阅正在处理的数据。

When you start WEKA the GUI chooser pops up and lets you choose four ways to work with WEKA and your data .

在启动 WEKA时，会弹出GUI选择器，让您选择使用WEKA和数据的四种方式。

At this point we are ready to create our model in WEKA .

现在，我们就准备好可以在 WEKA 内创建我们的模型了。

So let 's see how to get our data into a format that the WEKA API can use .

那么让我们看看如何将我们的数据转换成 WEKAAPI可以使用的格式。

Let 's put our data through the regression model and make sure the output matches the output we computed using the Weka Explorer .

让我们把我们的数据通过回归模型进行处理并确保输出与我们使用 WekaExplorer计算得到的输出相匹配。

Now that the desired model has been chosen we have to tell WEKA where the data is that it should use to build the model .

现在，选择了想要的模型后，我们必须告诉 WEKA它创建这个模型应该使用的数据在哪里。

Hopefully after reading this series you will be inspired to download WEKA and try to find patterns and rules from your own data .

希望，在阅读完本系列后，您能跃跃欲试地下载 WEKA并尝试从您自己的数据中找到模式和规则。

But this scratch gets you acquainted with the concept and suffice for our WEKA tests in this article .

但我们的简介让您充分熟悉了这个概念，已足够 应付本文中 WEKA 试用。