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Neural networks, as applied to data mining in the context of artificial intelligence and deep learning, are computational models inspired by the structure and function of biological neurons. These networks consist of interconnected nodes or "neurons" arranged in layers. The input layer receives data, which is then processed through hidden layers via weights and biases to produce output at the output layer. In long short-term memory (LSTM) networks, a special type of recurrent neural network, the connections between nodes form directed cycles, enabling the network to maintain an internal state or memory over time. This property makes LSTM networks particularly well-suited for handling sequential data and temporal dependencies, such as in natural language processing, speech recognition, and time series prediction. Neural networks in data mining serve as powerful tools for uncovering patterns and relationships within complex datasets. They can perform various tasks, including classification, regression, clustering, dimensionality reduction, and feature learning, thereby significantly contributing to the advancement of artificial intelligence and deep learning technologies.