Weka 3 - Data Mining with Open Source Machine Learning Software in Java

Weka Knowledge Explorer

The Weka Knowledge Explorer is an easy to use graphical user interface that harnesses the power of the weka software. Each of the major weka packages Filters, Classifiers, Clusterers, Associations, and Attribute Selection is represented in the Explorer along with a Visualization tool which allows datasets and the predictions of Classifiers and Clusterers to be visualized in two dimensions.

Clicking on each of the small images below will load a full sized version.

Preprocess Panel

The preprocess panel is the start point for knowledge exploration. From this panel you can load datasets, browse the characteristics of attributes and apply any combination of Weka's unsupervised filters. to the data.

Classifier Panel

The classifier panel allows you to configure and execute any of the weka classifiers on the current dataset. You can choose to perform a cross validation or test on a separate dataset. Classification errors can be visualized in a pop-up data visualization tool. If the classifier produces a decision tree it can be displayed graphically in a pop-up tree visualizer.

Cluster Panel

From the cluster panel you can configure and execute any of the weka clusterers on the current dataset. Clusters can be visualized in a pop-up data visualization tool.

Associate Panel

From the associate panel you can mine the current dataset for association rules using the weka associators.

Select Attributes Panel

This panel allows you to configure and apply any combination of weka attribute evaluator and search method to select the most pertinent attributes in the dataset. If an attribute selection scheme transforms the data then the transformed data can be visualized in a pop-up data visualization tool.

Visualize Panel

This panel displays a scatter plot matrix for the current dataset. The size of the individual cells and the size of the points they display can be adjusted using the slider controls at the bottom of the panel. The number of cells in the matrix can be changed by pressing the "Select Attributes" button and then choosing those attributes to displayed. When a dataset is large, plotting performance can be improved by displaying only a subsample of the current dataset. Clicking on a cell in the matrix pops up a larger plot panel window that displays the view from that cell. This panel allows you to visualize the current dataset in one and two dimensions. When the colouring attribute is discrete, each value is displayed as a different colour; when the colouring attribute is continuous, a spectrum is used to indicate the value. Attribute "bars" (down the right hand side of the panel) provide a convenient summary of the discriminating power of the attributes individually. This panel can also be popped up in a separate window from the classifier panel and the cluster panel to allow you to visualize predictions made by classifiers/clusterers. When the class is discrete, misclassified points are shown by a box in the colour corresponding to the class predicted by the classifier; when the class is continuous, the size of each plotted point varies in proportion to the magnitude of the error made by the classifier.

Interactive decision tree construction

Weka has a novel interactive decision tree classifier (weka.classifiers.trees.UserClassifier). Through an intuitive, easy to use graphical interface, UserClassifier allows the user to manually construct a decision tree by definining bi-variate splits in the instance space. The structure of the tree can be viewed and revised at any point in the construction phase.

Defining a bi-variate split on the iris data

Neural Network GUI

Weka also has a graphical user interface to a neural network (weka.classifiers.functions.neural.NeuralNetwork). This interface allows the user to specify the structure of a multi-layer perceptron and the parameters that control its training.