Weka Software For Mac

Welcome back to the course on Data Mining with Weka. I’m Ian, up here in New Zealand. First we’re going to download the Weka system. This is something you’re going to have to do on your computer. We’re going to download it from this URL.

Weka

Without delay, let’s go straight there. This is www.cs.waikato.ac.nz/ml/weka. You can read about Weka here. I’m going to go straight to the Download button and download and install Weka on my computer. I’m running on a Windows machine here, but there are versions down at the bottom you can see for Mac OS X and Linux and so on. You need to download the appropriate version for your machine.

Jan 25, 2013 - Weka is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any.

Weka Software Download For Mac

Wiki software for mac

I’m going to download a self-extracting executable without the Java Virtual Machine – I already have the Java Virtual Machine on my computer. I’m going to click here, but you’re going to need to do whatever’s appropriate for your computer. While it’s downloading, let’s have a word about the pronunciation of the word ‘Weka’. It’s called Weh-kuh. We don’t like calling it ‘weaker’ system.

It’s not ‘weaker’, it’s Weka, pronounced to rhyme with ‘Mecca’. That’s the name of the bird; that’s the name of our software. I think it has downloaded now, and I’m going to open it.

  • Weka packahe provides a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.
  • Weka-Parallel is a modification to Weka, created with the intention of being able to. Details weka outlier is an implementation of outlier detection algorithms for WEKA.

This is a standard setup wizard. I’m just going to keep clicking “Next”. Yes, I’m happy with the GNU Public License. I’m going to have a full install. I’m going to install it in the default place – you need to remember the name of this place; we’re going to need to visit there in a moment.

We’re going to install the whole thing. This is going to take a couple of minutes. I’m just off for a cup of coffee; I’ll be back in a second. Now, it’s installed. Let’s carry on here.

I want to click “Finish”, but actually I’m not going to start Weka. I’m going to uncheck that, and click “Finish”, because there are a couple of things I want to do first. Let’s go and see where Weka is. It’s on my computer in Program Files. I’m going to create a shortcut to that, because we’re going to be using it a lot in this course.

I’m just going to put it on the desktop. Then, I’m going to do one more thing.

I’m going to go inside this folder, and I’m going to look at the data folder. This contains a bunch of datasets we’re going to be using. I’m going to take this folder and copy it and put it somewhere convenient. Let’s cut that, and I’m going to put it in the “My Documents” folder. I’m going to rename it “Weka datasets”.

I’ve finished installing Weka. I’ve got my shortcut to Weka here.

Oops – I made my shortcut to the wrong place; I meant to make the shortcut to this here. Let me just make a shortcut here.

Create shortcut, put it on the desktop. That’s the one I want. Now, when I click here, it will open Weka.

Back to the slide. There are four interfaces in Weka. The Explorer is the one that we’ll be using throughout this course. We’re just using the Explorer, but also there’s the Experimenter for large-scale performance comparisons for different machine learning methods on different datasets. There’s the KnowledgeFlow interface, which is a graphical interface to the Weka tools; and there’s a command-line interface.

But we’re just going to use the Explorer. So let’s get on with it. Here’s the Explorer. Across the top, there are five panels: the Preprocess panel; the Classify panel (these are greyed out because I haven’t opened a file yet), where you build classifiers for datasets; Clustering, another procedure Weka is good at, although we won’t be talking about clustering in this course; Association rules; Attribute selection; and Visualization. In this course, we’ll be using mainly the Preprocess panel to open files and so on, the Classify panel to experiment with classifiers, and the Visualize panel to visualize our datasets. Here’s how to download the Weka data mining workbench and install it on your own computer. For future convenience, create a shortcut to the program and put it somewhere handy – like the desktop.

The Weka download comes with a folder containing sample data files that we’ll be using throughout the course; put this somewhere handy too – like the My Documents folder. Weka has four interfaces: the “Explorer” is the one we’ll be using throughout this course. ( In the video Ian downloads Weka 3.6, but you should download the latest stable version of the software.).

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