Besides explaining the most current theories, Solving Data Mining Problems through Pattern Recognition takes a practical approach to overall project development concerns. The rigorous multi-step method includes defining the pattern recognition problem; collection, preparation, and preprocessing of data; choosing the appropriate algorithm and tuning algorithm parameters; and training, testing, and troubleshooting. Pattern classification, estimation, and modeling are addressed using the following algorithms: linear and logistic regression, unimodal Gaussian and Gaussian mixture, multilayered perceptron/backpropagation and radial basis function neural networks, K nearest neighbors and nearest cluster, and K means clustering.

While some aspects of pattern recognition involve advanced mathematical principles, most successful projects rely on a strong element of human experience and intuition. Solving Data Mining Problems through Pattern Recognition provides a strong theoretical grounding for beginners, yet it also contains detailed models and insights into real-world problem-solving that will inspire more experienced users, be they database designers, modelers, or project leaders.