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Neural network data mining is the process of gathering and extracting data by recognizing existing patterns in a database using an artificial neural network. These artificial neural networks are networks that emulate a biological neural network, such as the one in the human body. Neural network data mining is used primarily by larger companies or research groups to gather and organize large databases, but it has numerous uses across several fields.
In humans, the neural network is based on neurons. Neurons are the conduits for the nervous system and are responsible for conducting sense experiences, such as pain and the sense of touch, throughout the body. They communicate through electrical and chemical means and neural networks. The messages they send move rapidly through the neural networks and can actually learn to conduct impulses in new ways, especially the neurons in the brain.
An artificial neural network is a description of a complex mathematical process that, in some respects, resembles its biological counterpart. The network is made up of artificial neurons, which are also complex mathematical equations, that function by moving information in an input and output process; this process mirrors how biological neurons work.
An artificial neural network (ANN) is a complex structure, but its main purpose is to calculate complex processes rapidly and efficiently, just like a human neural network. ANNs are also set up so they can learn by doing these processes, making them a form of artificial intelligence. They have a variety of practical uses and can be seen in everything from speech recognition software to radar systems.
ANNs are the key component of neural network data mining. They are able to examine large databases, known as data warehouses, and analyze and extract specific chunks of information through pattern recognition. What that chunk of information is depends on the needs of the user. In large companies, they often need to analyze data and notice trends, especially with regard to expenditures, marketing and sales.
In addition to large companies, another principal user of neural network data mining is the scientific and engineering community. These professionals can use data mining to examine large bits of information gathered in research and observation, and extract whatever patterns they need to from that data. This can save many hours of what would otherwise be an exhaustive process.
There are many other areas wherein neural network data mining is used. For example, it is used in gaming, such as in machines than play chess, and in areas of surveillance, such as domestic security that monitors trends in terrorist activity. More recently, it has been used in mining information about geographical systems, such as statistics vital to climate change.
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