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¡@¡@Customer Relationship Management (CRM) is a key element of modern marketing strategies.KDD CUP 2009 provides large-scale marketing databa from Fsesrance Telecom ORANGE company. Our purpose is able to predict customer's preferences. We are also able to provide then suggestion about surrounding Products and make them more profitably.We will use neural network analysis a large database.

So far of Neural network
still have a new theory of architecture which has been raised constantly. Because the computer increase in computing
capacity, it makes
neural networks more powerful. Neural network has input
layer, hidden layer and output layer.We
can find hidden layer's relations from the expected input and output data. We use an enormous amount
of data to calculus and a series of studing.

It makes results of neural network will be more and more precise. The different algorithms will also affect studys and results. We will able to create predictable platform from Neural Network that we prior had learned.The main characteristic of this platform is its ability to scale on very large datasets with hundreds of thousands of instances and thousands of variables. The rapid and robust detection of the variables that have most contributed to the output prediction can be a key factor in a marketing application.

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**Research background and motivation **

** ** The main purpose
create customer relationship management system and can Improve relations between customer and vendor. Vendor can
understand the requirement about customers and can improve vendor sales rate. Vendor offers statistical data about customer and the
database is too large . So we use
neural network to learn. Neural network have always played an important role in
research and application for many years. Their applicative areas include
networking, investment, engineering, management and etc. The most commonly use
algorithm in neural network which is the back-propagation(BP) learning.The more
common structure is feed-forward back-propagation algorithm. The calculational process
contains forward conduction and reverse transmission. The input signal be treated via weight and then to send to hidden layer.It can get output
value through the transfer function.
If the output value
and expectations are not same , reverse transmission and correct network
weights. It make error to reduce ,so it has
function of learning and recall. It can do training and
prediction.Because Neural model is
to keep on being trained, relatively the accurate prediction value is high. It is to master customer of preferences and
trends,and the market is one of the key.

**Researchful method**

¡]¤@¡^Neural network & algorithms

Neural Network(NN) is similar to
the structure of the human neural and it is base on study of brain and nervous
system. It not only start with information technology but also has the basic
characteristics of human brain : Learning, memory and induction. Neural
network has the following types of neurons

¡·processing element; PE

¡@summation function¡Bactivity function¡Btransfer function

¡·layer

¡@regular output¡Bcompetitive output¡B competitive learn

¡·network

learning course ¡X supervised Learning Network¡BUnsupervised Learning Network¡BAssociation learning network¡BBidirectional associative learning network¡Boptimum learning network

recalling course
¡Xsupervised recalling Network¡BUnsupervised recalling Network¡BAssociation recalling
network¡BBidirectional
associative recalling network¡Boptimum recalling
network

¡·Multi-input neuron

_{} _{} output (a) = f ( WP + b
)

The basic principles of Back-propagation network is to use
the concept of Gradient Steepest Descent Method to minimize error function.
That is, back-propagation network is used to let the output layer unit error,
layer by layer to let the input layer back-propagation to different layer unit.
At all layer obtain a referential error and it link to the weight to adjust the
value of the corresponding. It can minimize the error between output value and
target value. We suppose that neural network's training are defined as
follows typomorphic :

The input variables is x. The Target variables is t. The variables p is total number of training. If the type of neural network has been trained ,the
actual output and target output are different. The back-propagation (BP) algorithm will be repeated through the data to
train neural network. it adjust link's weights
and bias weights until they reach the goal of set. Our goal is to minimize functional error as follows.

The
output layer variables is . The variables is a output layer of
inferential value. The Back-propagation
network is to use Gradient Steepest Descent Method to minimize error function. In order to reduce the amount of error, it must be of the opposite direction adjust link's
weights of the network; Link-weighted adjust variable
value of relationship as follows:

The variables is learn's rate. After we through chain rate to calculate,link weights can
be adjusted to change the amount of rewriting of the following results.

One of the _{} and _{} return value to
layer, the complete expression is as follows.

We can get the weighted
express which contains the inertia link

The
hidden layer is as follows

The
output hidden layer is as follows

One of the
inertia factor usually is value between 0.1 to 0.8 range.

Therefore, as long as I give neural network a error tolerance , a studying the rate of , a inertia factor and the largest study circle
,and then we can adjust the network by weight to achieve neural network for the
customer data mining.

(¤G) researchful situation

we will be screening on variables because the variable is
too many. SPSS process datas very well for the current and SPSS have many statistical methods itself. We
used SPSS to analysis each the relationship between variable and target. If the
relation is much better , it will affect the output result. After treatment of
the information,we use neural network to learn in NNTOOL of MATLAB. The calculated
capability of MATLAB is a very strong software. It have many function itself
,so we writing the program more convenience. There are many algorithms in
NNTOOL .Ddepend on the difference algorithm will effect the learn efficiently
and prediction.We use the BP algorithm here and this is only a preliminary
decision.

(¤T) Algorithm problem

The input has 15000 variables ,the
target is only -1 and 1. The
transfer function use the hardlims.
We discover that will produce problem to revise weigh.The output value and
expectations will only appear -2,0 and 2. We get the information variables that
is too large. Too many data amounts cause many softwares can¡¦t work , looking
for new ways to solve this problem

**Expected
to achieve**

We will solve problem of database at present and the neural network has trained. The ultimate goal is to achieve accurate prediction.