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Data Mining and Knowledge Discovery Handbook, 2 Edition part 45

Data Mining and Knowledge Discovery Handbook, 2 Edition part 45. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 420 G. Peter Zhang 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 modelbased 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 Chen and Chen 1995 Cybenko 1989 Hornik Stinch-combe and White 1989 . 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 nonparametric nature are more cable for modeling complex data mining problems. Finally neural networks are able to solve problems that have imprecise patterns or data containing incomplete and noisy .