How Neural Networks Can Be Used For Data Mining?

As all of us are aware that how technology is growing day-by-day and a Large amount of data is produced every second, analyzing data is going to be very important because it helps us in fraud detection, identifying spam e-mail, etc. So Data Mining comes into existence to help us find hidden patterns, discover knowledge from large datasets.

In this article, we basically look at Neural networks and what is the application of neural networks for Data Mining work.

Neural Network:

Neural Network is an information processing paradigm that is inspired by the human nervous system. As in the Human Nervous system, we have Biological neurons in the same way in Neural networks we have Artificial Neurons which is a Mathematical Function that originates from biological neurons. The human brain is estimated to have around 10 billion neurons each connected on average to 10,000 other neurons. Each neuron receives signals through synapses that control the effects of the signal on the neuron. 

How Artificial Neural Network Work?

Let us Suppose that there are n input like X1,X2,…,Xn to a neuron.

=> The weight connecting n number of inputs to a neuron are represented by [W]=[W1,W2,..,Wn].

=> The Function of summing junction of an artificial neuron is to collect the weighted inputs and sum them up.

      Yin=[X1*W1+X2*W2+….+Xn*Wn]

=> The output of summing junction may sometimes become equal to zero and to prevent such a situation, a bias of fixed value Bo is added       to it.

     Yin =[X1*W1+X2*W2+….+Xn*Wn] + Bo  

    // Yin then move toward the Activation Function.

=> The output Y of a neuron largely depends on its Activation Function (also known as transfer function).

=> There are different types of Activation Function are in use, Such as

1. Identity Function

2. Binary Step Function With Threshold

3. Bipolar Step Function With Threshold

4. Binary Sigmoid Function 

5. Bipolar Sigmoid Function 

Neural Network Architecture:

While there are numerous different neural network architectures that have been created by researchers, the most successful applications in data mining neural networks have been multilayer feedforward networks. These are networks in which there is an input layer consisting of nodes that simply accept the input values and successive layers of nodes that are neurons as depicted in the above figure of Artificial Neuron. The outputs of neurons in a layer are inputs to neurons in the next layer. The last layer is called the output layer. Layers between the input and output layers are known as hidden layers.

As you know that we have two types of Supervised learning one is Regression and another one is classification. So in the Regression type problem neural network is used to predict a numerical quantity there is one neuron in the output layer and its output is the prediction. While on another hand in the classification type problem the output layer has as many nodes as the number of classes and the output layer node with the largest output values gives the network's estimate of the class for a given input. In the special case of two classes, it is common to have just one node in the output layer, the classification between the two classes being made by applying a cut-off to the output value at the node.    

Why use Neural Network Method in Data Mining?

Neural networks help in mining large amounts of data in various sectors such as retail, banking (Fraud detection), bioinformatics(genome sequencing), etc. Finding useful information for large data which is hidden is very challenging and very necessary also. Data Mining uses Neural networks to harvest information from large datasets from data warehousing organizations. Which helps the user in decision making.

Some of the Applications of Neural Network In Data Mining are given below:

• Fraud Detection: As we know that fraudsters have been exploiting businesses, banks for their own financial gain for many past years, and the problem is going to increase in today's modern world because of the advancement of technology, which makes fraud relatively easy to commit but on the other hand technology also helps is fraud detection and in this neural network help us a lot in detecting fraud.
• Healthcare: In healthcare, Neural Network helps us in Diagnosing diseases, as we know that there are many diseases and there are large datasets having records of these diseases. With neural networks and these records, we diagnosed these diseases in the early stage as soon as possible.

Different Neural Network Method in Data Mining

Neural Network Method is used For Classification, Clustering, Feature mining, prediction, and pattern recognition. McCulloch-Pitts model is considered to be the first neural network and the Hebbian learning rule is one of the earliest and simplest learning rules for the neural network. The neural network model can be broadly divided into the following three types: 

• Feed-Forward Neural Networks: In Feed-Forward Network, if the output values cannot be traced back to the input values and if for every input node, an output node is calculated, then there is a forward flow of information and no feedback between the layers. In simple words, the information moves in only one direction (forward) from the input nodes, through the hidden nodes (if any), and to the output nodes. Such a type of network is known as a feedforward network.
• Feedback Neural Network: Signals can travel in both directions in a feedback network. Feedback neural networks are very powerful and can become very complex. feedback networks are dynamic. The "states" in such a network are constantly changing until an equilibrium point is reached. They stay at equilibrium until the input changes and a new equilibrium needs to be found. Feedback neural network architectures are also known as interactive or recurrent. Feedback loops are allowed in such networks. They are used for content addressable memory.
• Self Organization Neural Network: Self Organizing Neural Network (SONN) is a type of artificial neural network but is trained using competitive learning rather than error-correction learning (e.g., backpropagation with gradient descent) used by other artificial neural networks. A Self Organizing Neural Network (SONN) is an unsupervised learning model in Artificial Neural Network termed as Self-Organizing Feature Maps or Kohonen Maps. It is used to produce a low-dimensional (typically two-dimensional) representation of a higher-dimensional data set while preserving the topological structure of the data.