Name: 用Weka進行數據挖掘(1.1：介紹) (Data Mining with Weka (1.1: Introduction))
Uploaded: 2021-01-14T06:00:23.000Z
Duration: 9 min
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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

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

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

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

that are useful in the real world. Let me

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

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

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

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'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

These coupons are kind of a mechanism for personalized pricing.

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.

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

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

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

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

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

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

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

the kiwi, found only in the islands of 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,

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.

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

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.

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

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

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用Weka進行數據挖掘(1.1：介紹) (Data Mining with Weka (1.1: Introduction))

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