Skip to 0 minutes and 11 secondsHi! 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. Without delay, let’s go straight there. Here we are. 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.
Skip to 0 minutes and 55 secondsI’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.
Skip to 1 minute and 16 secondsWhile 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. Weka.
Skip to 1 minute and 36 secondsI think it has downloaded now, and I’m going to open it.
Skip to 1 minute and 43 secondsThis 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.
Skip to 2 minutes and 18 secondsNow, 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.
Skip to 2 minutes and 43 secondsI’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.
Skip to 2 minutes and 58 secondsThen, 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.
Skip to 3 minutes and 20 secondsLet’s cut that, and I’m going to put it in the “My Documents” folder.
Skip to 3 minutes and 29 secondsI’m going to rename it “Weka datasets”.
Skip to 3 minutes and 40 secondsI’m all set. I’ve finished installing Weka.
Skip to 3 minutes and 49 secondsI’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.
Skip to 4 minutes and 10 secondsNow, 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.
Skip to 4 minutes and 49 secondsAcross 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.
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