Description: The research process Data Mining and Neural Networks Final Seminar Report explains what data mining is. It also shows connectivity between data mining and neural networks. The last century and the current scenario have been witnessing a colossal increase in the amount of shared information. There has been an information explosion. With giant strides made in almost all the fields from education, IT, business, space research and many more there is a gigantic bulge of information every day. Predictions and scientific forecasts say that the trend is going to double every year and perhaps triple will be the amount of data that needs storing. And perhaps even more than that.
In a situation like this there is a greater need and demand to come up with efficient database management systems. This paper not only comments on the current effective trends of data mining but also future prospects. The research paper talks about the connecting link between data mining and neural networks and explains how effective functioning is achieved.
Clementine User Guide, a data mining toolkit describes data mining thus: ‘”using a variety of techniques to identify nuggets of information or decision-making knowledge in bodies of data, and extracting these in such a way that they can be put to use in the areas such as decision support, prediction, forecasting and estimation. The data is often voluminous, but as it stands of low value as no direct use can be made of it; it is the hidden information in the data that is useful”What is Neural Networks:
Neural Networks is an application that is analogous to the functioning of brain. A neural network is something like a neuron in the brain and this identifies a pattern in the data base. A neural network thus can identify patterns just as people do. Hence large databases can be effectively managed using neural networks.Conclusion:
The research abstract suggests the strides made by data mining by employing the applications used by neural networks. NN also increases the efficiency and security levels of a data mining operation.
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