Data Mining with Weka. I'm Ian Witten from the University of Waikato

in New Zealand and I'm presenting the videos for this course which is being prepared

by the Department of Computer Science

at the University of Waikato. Data mining is a mature technology that a lot

of people are

beginning to take very seriously, and a lot of other people find it very mysterious.

The real aim of this course is to take the mystery out of data

mining. This is a practical course on how to use the Weka

workbench, which you will download as part of the course,

for data mining. We explain the basic principles of several popular

algorithms and how to use them in practical applications.

In the world today, we're overwhelmed with data,

every time you swipe your credit card, every item you checkout out at the

supermarkets

every time you send a text, make a phone call, or send an email,

or type a key on a computer even, every time you walk past a security camera.

It all generates a little bit of data in a database. Data mining is about going

from the raw data to information. Information that can be used to make

predictions

that are useful in the real world. Let me

give you an example. You're at the supermarket checkout.

The till records every item you bought.

At the end, you hand over your loyalty card, and they give you a couple of percent off,

and you give them your name and address and, indirectly, access to all sorts of

demographic information about you

and people like you. Everybody likes a good bargain.

It's been a good day today, because thanks to those coupons they sent you in

the mail last week,

you've been able to stock up on some things you wouldn't normally have bought,

but that you bought today because they are such a good deal. Next week, they'll send

you some more coupons, and

you'll go shopping again and buy some more stuff. They do some experiments on you,

you know, they try to figure out how much more you would buy if the

price was just that little bit less.

These coupons are kind of a mechanism for personalized pricing.

They have access to all sorts of data

from you and people like you, in order to do these experiments and figure these things out.

Everybody wins: you get your bargains, they sell more stuff.

It sounds like a good deal to me. Here's another application.

Suppose you and your partner want a child, but you can't have one.

It's fun trying, but it can get

a little bit frustrating, and, ultimately, very frustrating, perhaps even tragic.

In artificial insemination, they

take some eggs from the woman's ovaries, and then they fertilize them with partner or donor

sperm, and then, they select from amongst the embryos produced

some to implant back into the womb.

You want to select the ones with the best chance of success

of producing a live birth, but you don't want too many

live births. The embryologist has access to all sorts of data

on these embryos. I think there are 50-100

pieces of information that they record about individual embryos, and they have

historical data on which ones produced a live birth,

success. So here's an ideal situation for data mining. We have lots of

historical data

we have data on the present situation, and we want to select

those embryos that have the best chance of success. Now, that's a good application

for data mining,

bringing a live child to couple who wants one.

I talk about data mining and machine learning. Data mining is the

application, and machine learning is the algorithms we use. We're talking about using

machine learning algorithms

for the purposes of data mining.

This is Data Mining with Weka, so the next question is “What's Weka?”

This is a weka here, this little bird.

It's a flightless bird, kind of like its better known cousin

the kiwi, found only in the islands of New Zealand.

This is what it sounds like,

coming to you from New Zealand.

However, in our context, Weka is a data mining workbench. It's an acronym for the

Waikato Environment for Knowledge Analysis. We just call it Weka.

It contains a large number of algorithms for classification,

and a lot of algorithms for data preprocessing, feature selection,

clustering,

finding association rules, things like that. It's a very

comprehensive workbench, and it's free, open source software

that you will download as part of this course in the next lesson.

It runs on

any computer. It's written in Java, runs on Linux, Windows, Mac.

You'll be able to download it and run it on your workstation and use it

during the course. You're going to learn how to load data into Weka and look at it;

you're going to learn about preprocessing, cleaning up data using filters;

exploring it using visualizations, applying classification algorithms,

interpreting the output, understanding evaluation methods,

evaluation is very important in this area, understand various representations for

models, and how popular

machine learning algorithms work, and be aware of common pitfalls

with data mining. The ultimate goal really is to empower you to use Weka on your

own data, and, most importantly, to understand what it is you are doing.

This is the first class. In this class,

you're going to get started with Weka. You're going to install it;

you're going to explore the Weka Explorer interface

and explore some data sets; build a classifier;

interpret the output of the classifier; use filters;

and visualize your data set. There's lots of things to do

in this class. Here's the structure of the course.

There are five classes altogether. Each class

consists of about six lessons.

Class 1 is Getting started with Weka. Then, we're going to look at Evaluation

in Class 2,

Simple classifiers in Class 3, More classifiers in Class 4, and Putting it all together

in Class 5. These are the six lessons

in Class1. Each lesson comprises a short video,

5-10 minutes, like this one, followed by an activity.

An activity that involves you doing something yourself.

You don't learn by me talking to you. You learn by actual doing things.

So, we have lots of activities for you

that involve using the Weka workbench. In the middle of the class is a mid-class

assessment, and at the end there is

a post-class assessment. The marks for these are combined, and if you get

more than 70%, you will get a signed certificate from the

University of Waikato

certifying that you have completed this course.

The activities are an important part of the course, but they are not part of the

assessment.

We really think you should do the activities, but you don't have to do them

for assessment purposes.

it's up to you. As well as that, associated with the course is a textbook

called Data Mining. It discusses data mining and Weka

in depth. It's a great book; I know it's a great book because I wrote it

myself

with a couple of friends. The publisher has kindly agreed to make available

large chunk this textbook to you

online so that you can use it for background reading.

It's only background reading; you don't have to read the textbook.

It's just if you want to delve into some of the ideas and algorithms in

more depth.

That's what it's there for. What you

need to do are the activities and the assessments,

and watch the videos, of course. That's it. I just thought I'd show you were I am.

I'm in New Zealand, that's where Weka is from.

That's where I'm sitting right now. This is the world as we see it in New Zealand.

We're at the top, you're probably down at the bottom somewhere.

We're in the top, in the center, and that arrow to the North Island of New Zealand

is where the University of Waikato is.

That's it for now. There is an activity associated with this lesson,

so go ahead and do it. Of course, you haven't learned very much in this lesson, so

it's not a very important activity.

Don't worry about it too much. You're not expected to do a lot of reading to do

this activity.

Just have a go and see how you get on,

and I'll see you again in the next lesson. I'm looking forward to that.

Goodbye for now.