The neural network has a high acceptance ability for noisy and high accuracy and is preferable in data mining. A number of advances in technology and business processes and some companies have contributed to a growing interest in data mining in both the public as well as from private sectors. Some of the changes include the growth of the computer network, which can be used to connect the database. So that will be possible using the neural network. So here this article gives the advantages of neural networks in data mining to better understand this topic. You also check out the advantages and disadvantages of neural networks to better understand this topic.
Advantages of a neural network in data mining:
- The neural network has a high acceptance ability for noisy and high accuracy and is preferable in data mining.
- A neural network is very suitable for solving the problem of data mining. Because of it a good characteristic of robustness, Self-organizing adaptive, parallel processing, distributing storage, and high degree fault tolerance.
- As compared to the statistical method, NN is useful especially when there is no prior knowledge about the analyzed data. They offer a powerful and distributed computing architecture, with the use of significant learning abilities. They are able to represent highly nonlinear and multivariable relationships among them.
- Neural networks have the ability to learn themselves and produce the output that is not limited to the input provided to them.
- The use of neural networks in data mining is a promising field of research and technology especially given the ready availability of large mass of data sets. The reported ability of the neural networks to detect and assimilate the relationship between a large number of variables.
- The input is stored in its own network instead of a database, hence the loss of data does not affect its working.