Weka 简明教程

Weka - Launching Explorer

在本章中，我们来看看 Explorer 为处理大数据提供的各种功能。

In this chapter, let us look into various functionalities that the explorer provides for working with big data.

当你在 Applications 选择器中单击 Explorer 按钮时，它将打开以下屏幕 −

When you click on the Explorer button in the Applications selector, it opens the following screen −

explorer button

在顶部，你会看到几个选项卡，如下所示 −

On the top, you will see several tabs as listed here −

  1. Preprocess

  2. Classify

  3. Cluster

  4. Associate

  5. Select Attributes

  6. Visualize

在这些选项卡下，有几个预先实现的机器学习算法。让我们现在详细地研究每个算法。

Under these tabs, there are several pre-implemented machine learning algorithms. Let us look into each of them in detail now.

Preprocess Tab

最初当你打开 Explorer 时，只有 Preprocess 选项卡处于启用状态。机器学习的第一步是预处理数据。因此，在 Preprocess 选项中，你将选择数据文件，处理它并使其适合应用各种机器学习算法。

Initially as you open the explorer, only the Preprocess tab is enabled. The first step in machine learning is to preprocess the data. Thus, in the Preprocess option, you will select the data file, process it and make it fit for applying the various machine learning algorithms.

Classify Tab

Classify 选项卡为你提供了用于分类数据的几种机器学习算法。简单列举几个，你可以应用诸如线性回归、逻辑回归、支持向量机、决策树、RandomTree、随机森林、朴素贝叶斯等算法。列表非常详尽，提供了有监督和无监督的机器学习算法。

The Classify tab provides you several machine learning algorithms for the classification of your data. To list a few, you may apply algorithms such as Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees, RandomTree, RandomForest, NaiveBayes, and so on. The list is very exhaustive and provides both supervised and unsupervised machine learning algorithms.

Cluster Tab

在 Cluster 选项卡下，提供了多种聚类算法 - 如 SimpleKMeans、FilteredClusterer、HierarchicalClusterer 等。

Under the Cluster tab, there are several clustering algorithms provided - such as SimpleKMeans, FilteredClusterer, HierarchicalClusterer, and so on.

Associate Tab

在 Associate 选项卡下，你会找到 Apriori、FilteredAssociator 和 FPGrowth。

Under the Associate tab, you would find Apriori, FilteredAssociator and FPGrowth.

Select Attributes Tab

Select Attributes 允许你根据多种算法（如 ClassifierSubsetEval、PrinicipalComponents 等）进行特征选择。

Select Attributes allows you feature selections based on several algorithms such as ClassifierSubsetEval, PrinicipalComponents, etc.

Visualize Tab

最后， Visualize 选项允许你对处理后的数据进行可视化，以便分析。

Lastly, the Visualize option allows you to visualize your processed data for analysis.

正如你所见，WEKA 为测试和构建机器学习应用程序提供了多种开箱即用的算法。为了有效地使用 WEKA，你必须对这些算法、它们的工作原理、在什么情况下选择哪种算法、它们的处理输出中需要注意什么等有深入的了解。简而言之，你必须在机器学习方面有扎实的基础，才能在构建应用程序时有效地使用 WEKA。

As you noticed, WEKA provides several ready-to-use algorithms for testing and building your machine learning applications. To use WEKA effectively, you must have a sound knowledge of these algorithms, how they work, which one to choose under what circumstances, what to look for in their processed output, and so on. In short, you must have a solid foundation in machine learning to use WEKA effectively in building your apps.

在接下来的章节中，你将深入学习资源管理器中的每个选项卡。

In the upcoming chapters, you will study each tab in the explorer in depth.