 
Hello everyone in this video tutorial we are going to
look into the graphic user interface
of the clustering application of neural network toolbox in matlab
so it can be started in 3 ways
type
and click on clustering app
or go to the app step and click on
neural network clustering
or you can directly type
so this is the welcome window of the
neural network clustering application
it's says that in clustering application of neural network you can use the neural network
 
and on the right side you can see the representation
neural network for this clustering application
so as you can see this neural network
is using the self organizing map
and this is not as same as that of the normal
feed propagation neural network
this is the type of unsupervised learning
we supply just the input and no targets are supplied
so there will be no back propagation
so what self organizing map is trying to do
is mimic the input data
that will provide and also try to copy their features
so let's look briefly
how self organizing map do it
first we supply number of cluster centers
also called the neurons and then specify
how these neurons will be arranged with the help of topology function
 
 
 
you can find more about these function
before each of these name and sending
next we need to find the distance
between the neurons we have specified
so the neurons in the space represented by
weight so as in feed propagation neural network
weight here does not represent the intensity of neural network transfer
instead the represent the coordinates of neurons
so what we will do is
find the distance between the coordinates
weight and the input coordinates
and we will try to minimize the coordinates
self organizing map can mimic the
by the input data set
so the distance is calculated by the usual
mean square error formula
and the neuron between the distance
as the wining neuron and the weight of winning neuron
is updated so that it get
closer to that of the input data set
but unlike that of the competitive layer
neural network in this case
also get their weight updated
formula for weight updated is given by this equation
in this equation
alpha represented learning rate
and the term in bracket is represent the distance between
the input point and weight is the cordinate of the neuron
the number of neurons is the neighbor of neuron
decided with the help of this function
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
