Hi, students! It's me, Dr. Loh. Today
we will look at the introduction to the WEKA software.
Actually, the name WEKA
comes from a bird; a species of bird from New Zealand.
So, what is WEKA?
The full name for Weka is Waikato Environment for Knowledge Analysis.
W for Waikato, E: Environment, K: Knowledge, A: Analysis.
As mentioned it is a name of a bird species found in the New Zealand.
So, WEKA now is a tool for
machine learning or data mining software,
developed by a group of scientists from
University of Waikato, New Zealand.
So the whole software is written in Java
programming package. It's a collection of machine learning algorithms.
And where
can you download the software?
You can go to this link and the Installer is there.
I will show you the link here.
Go to the link here.
There are lots of information related to WEKA in this page.
So you can download the software at this step: "Download and install".
So here there are several platforms for installation.
So usually we install with the Windows.
So you can click there and follow the
steps mentioned.
So, the Installer will be downloaded to
your laptop or your PC.
Apart from installation, you can learn
a lot of things from the webpage.
Ok?
We have books related to WEKA.
There are slides. Ok ...and highlights on the
books.
Chapter PowerPoint slides.
And then we have the courses, ...classes on Data Mining using WEKA.
There are a series of courses available. So maybe you can follow the courses.
The blog.
WIKI.
If you are new to WEKA, there
are a lot of things to explore.
We have 'downloading and installation' of
WEKA here. The requirement, documents, ...
troubleshooting, format of WEKA and so on.
And the available datasets.
These are datasets publicly available
from the Internet.
Ok?
So you can run a lot of sample data available here...to understand more from WEKA.
OK?
Now the interesting part is what
can WEKA do?
So the main features stored in WEKA include data pre-processing tools
which is the first stage of the data analysis where you need to inspect on the data qualitative aspects.
Ok, then we have the learning
algorithms where we look into
classification, clustering, then we
evaluate the methods which algorithm is better than another.
And the famous interface stored in WEKA is the GUI, Graphical User Interfaces.
Here, we can look into data visualization.
We can interchange the y-axis and the x-axis graphs, whether you like to look at certain attributes and so on.
We can also do programming into WEKA software.
You can write your own Java code to program on certain analyses of data.
Then there is also environment to compare the learning algorithms.
So which algorithm is superior and which is more reliable.
When you have installed the WEKA, you will find this icon.
So opening on the icon ...
will bring you to the main page of WEKA GUI 
(Grafic User Interface).
You can see on this screen there are 5 applications available; the Explorer, Experimenter, Knowledge Flow, Workbench, and Simple CLI.
Under Explorer, we can run analysis on the data. You can play around with the data.
So if we go into Explorer...
Once, you've opened the Weka Explorer, it
will bring you to this page.
So we have 6 available tabs here: Preprocesses, Classify, Cluster, Associate, Select attributes, and Visualize.
So when you point to Preprocess, it mentions that you need to open your file.
So this is the first stage where you need to do data preprocessing.
You need to clean your data,
you need to analyze the statistics and so on.
Right. The remaining tabs are not
available at the moment because we have not opened any dataset yet.
When you have opened some data files, then the remaining tabs will be available.
So you can open the files and look into the section where you store your data files for analysis.
Classify is the section where you need
to group your data into predefined classes.
We need to have the class labels
to apply this mode.
Move on to the Cluster tab is where you need to group your data without knowing what are the classes.
Without any predefined labels, so
we only managed to group them by certain same patterns of the data.
Then, we have the Associate tab.
Associate is to find the association or the rules.
Select attributes is a section where we can select important attributes or relevant attributes
and run selective algorithms based on 
the attributes selected for the analysis.
Then, we have the Visualize tab where we can analyze the relationship among different attributes.
So basically we visualize by dotted points.
You can interchange the y-axis or x-axis and so on.
We will look into this in the
later videos.
Under the Experimenter, we design experiments on the data sets and the algorithms used in Explorer.
So, we can work out the statistical significant analysis on different algorithms used.
Whether which algorithm perform better
significantly than another and so on.
And then we can look into the datasets.
This page will appear when you have opened up the WEKA experiment.
So there are 3 tabs available here: 
Setup, Run, and Analyze.
So once you have opened your dataset, the Run tab will be available.
So there are different modes of experiments.
And then you have different Experimental types, Iteration Control, Algorithms to run datasets,
comparing several datasets and
so on.
If we go into the knowledge flow, we will see this page.
So there are a long lists of designs available here.
We can actually expand all this.
And there are many many options
available here.
In WEKA Knowledge flow, usually, we will pull out the diagrams and connect all the processes in a sequence of knowledge process flow.
So, we can even execute from the flow chart itself to run the WEKA Explorer results.
The WEKA Workbench is a new interface.
It's actually combined several interfaces like Explorer, Experimenter, Knowledge Flow, ... 
everything on the same page.
So this is helpful if we are looking at too many pages of interfaces at the same time.
So we can see the WEKA Workbench page here.
You can see that it is actually a combination of this
Explorer part, WEKA Explorer, ...
Experiment part, the Data mining process
part and the Simple CLI.
So it is actually a combination of several interfaces on a single page.
The last interface we can see here is the simple Command Line Interface, CLI.
So this is an option where we can run the WEKA functions directly by giving commands.
We go into the simple CLI tab.
The screen appears like this.
So it is a simple page where you can type the commands and WEKA will execute
accordingly to the commands.
