**Neural networks or Artificial neural networks** (**ANNs**) or **Connectionist systems** are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems “learn” to perform tasks by considering examples, generally without being programmed with any task-specific rules. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as “cat” or “no cat” and using the results to identify cats in other images. They do this without any prior knowledge about cats, e.g., that they have fur, tails, whiskers and cat-like faces. Instead, they automatically generate identifying characteristics from the learning material that they process.

ANN is based on a collection of connected units or nodes called artificial neurons which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it.

Neural networks are computing models for information processing and are particularly useful for identifying the fundamental relationship among a set of variables or patterns in the data. They grew out of research in artificial intelligence; specifically, attempts to mimic the learning of the biological neural networks especially those in human brain which may contain more than 1011 highly interconnected neurons. The popularity of neural networks is due to their powerful modeling capability for pattern recognition. Several important characteristics of neural networks make them suitable and valuable for data mining. First, as opposed to the traditional model based methods, neural networks do not require several unrealistic a priori assumptions about the underlying data generating process and specific model structures. Rather, the modeling process is highly adaptive and the model is largely determined by the characteristics or patterns the network learned from data in the learning process. This data-driven approach is ideal for real world data mining problems where data are plentiful but the meaningful patterns or underlying data structure are yet to be discovered and impossible to be pre-specified. Second, the mathematical property of the neural network in accurately approximating or representing various complex relationships has been well established and supported by theoretic work. This universal approximation capability is powerful because it suggests that neural networks are more general and flexible in modeling the underlying data generating process than traditional fixed-form modeling approaches. As many data mining tasks such as pattern recognition, classification, and forecasting can be treated as function mapping or approximation problems, accurate identification of the underlying function is undoubtedly critical for uncovering the hidden relationships in the data. Third, neural networks are nonlinear models. As real world data or relationships are inherently nonlinear, traditional linear tools may suffer from significant biases in data mining. Neural networks with their nonlinear and non-parametric nature are The popularity of neural networks is due to their powerful modeling capability for pattern recognition. Several important characteristics of neural networks make them suitable and valuable for data mining. First, as opposed to the traditional model based methods, neural networks do not require several unrealistic a prior assumptions about the underlying data generating process and specific model structures. Rather, the modeling process is highly adaptive and the model is largely determined by the characteristics or patterns the network learned from data in the learning process. This data-driven approach is ideal for real world data mining problems where data are plentiful but the meaningful patterns or underlying data structure are yet to be discovered and impossible to be pre-specified.