Artificial Neural Networks or just called neural networks had been successfully applied to an assortment of supervised and unsupervised learning methods and applications until now. However, these are not very commonly used for tasks and applications related to data-mining because they often require long training processes and also frequently generate impenetrable models.
But there are few neural-network learning algorithms or Neural Network In Data Mining that is capable of producing understandable and comprehensible models which do not require excessive training times. Specifically, two types of approaches are used for data mining with neural networks, and we’re here to discuss those methods.
The first type of method which is defined as ‘rule extraction,’ involves figurative mining models from the trained neural networks, while the later approach always aims at directly learning the easy-to-understand and simple interfaces. Seeing those differences and usefulness of both methods; neural networks must deserve a secure place in the state-of-the-art models used by the data-mining specialists.
Real-Life Applications of Neural Network
- The methods and applications of Neural Network In Data Mining are applied to are liable to some broad categories including:
- Regression analysis or function approximation including time series prediction and fitness approximation or modelling
- Classification of the neural network including recognition of pattern and sequence, sequential decision making, and novelty detection
- Data processing and mining including clustering, filtering, compression, and blind source separation
- Robotics including computer numerical control and directing manipulators
If previously, the information was a commodity with a value limited to its instantly accessible features, now it is a resource the value of which depends on one’s skill to interpret it - the ability to make the most out of the available information.
This process requires complex systems that consist of multiple layers of algorithms, that together construct a network inspired by the way the human brain works, hence its name - neural networks.
Vector is an abstract representation of raw data that reiterates its meaning into a comprehensive form for the machine.
Unlike other types of neural networks that process data straight, where each element is processed independently of the others, recurrent neural networks keep in mind the relations between different segments of data, in more general terms, context.
Just like traditional Artificial Neural Networks, RNN consists of nodes with three distinct layers representing different stages of the operation.
Overall, the RNN neural network operation can be one of the three types:
