weka

The Data Mining Research of Library which base on WEKA

基于WEKA软件的图书馆数据挖掘研究

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

Analysis on Weka foundation classes and algorithm extending method

Weka平台基础类分析及算法扩展方法

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。

Second developing of data mining tool Weka .

数据挖掘工具Weka的二次开发。

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。

At this point , please go ahead and install WEKA .

我们继续并安装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。

Study to web transactions clustering algorithm on WEKA

基于WEKA平台的Web事务聚类算法的研究

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。

Let 's get some real data and take it through its paces with WEKA .

让我们现在开始获得一些真正的数据并将其带入WEKA。

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。

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。

Using WEKA data mining software for comparison of classification algorithm , and finally developed a real-time motion classification application .

利用WEKA数据挖掘软件进行分类算法的对比，最后根据分类效果好的算法在手机上开发了一款实时运动分类应用程序。

So now we have the data loaded into WEKA .

现在我们已经将数据载入了WEKA。

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。

Creating the regression model with WEKA

用WEKA创建一个回归模型

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。

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。

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。

When you do it yourself , you 'll see that WEKA flies through the model in less than a second .

在您自已生成这个模型时，您会看到WEKA只用了不到一秒的时间就处理好了这个模型。

Finally , the last point I want to raise about classification before using WEKA is that of false positive and false negative .

在使用WEKA前，有关分类我还想指出最后一点，那就是假正和假负。

The math behind the method is somewhat complex and involved , which is why we take full advantage of the WEKA .

此方法背后的算法多少有些复杂和难懂，这也是我们为何要充分利用WEKA的原因。

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。

WEKA is powerful and100-percent free to use .

WEKA功能强大且100%免费。

NOTE : I would warn you ahead of time that the WEKA API can be difficult to navigate at times .

注：我最好提前告诫您WEKAAPI有时很难导航。

Building the data det for WEKA

为WEKA构建数据集

Bigger houses reduce the value & WEKA is telling us that the bigger our house is , the lower the selling price ?

较大的房子价格反而低&WEKA告诉我们房子越大，销售价格越低？

As you 've seen , WEKA can do many of the data mining tasks that were previously available only in commercial software packages .

正如您所见，WEKA可以完成很多在商业软件包中才能完成的数据挖掘任务。

It 's actually quite easy to put our data through the regression model using the WEKA API , far easier than actually loading the data .

实际上使用WEKAAPI让数据通过回归模型得到处理非常简单，远简单于实际加载数据。