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<p><b>Abstract</b>—Extracting patterns and models of interest from large databases is attracting much attention in a variety of disciplines. Knowledge discovery in databases (KDD) and data mining (DM) are areas of common interest to researchers in machine learning, pattern recognition, statistics, artificial intelligence, and high performance computing. An effective and robust method, coined regression-class mixture decomposition (RCMD) method, is proposed in this paper for the mining of regression classes in large data sets, especially those contaminated by noise. A new concept, called “<it>regression class</it>” which is defined as a subset of the data set that is subject to a regression model, is proposed as a basic building block on which the mining process is based. A large data set is treated as a mixture population in which there are many such regression classes and others not accounted for by the regression models. Iterative and genetic-based algorithms for the optimization of the objective function in the RCMD method are also constructed. It is demonstrated that the RCMD method can resist a very large proportion of noisy data, identify each regression class, assign an inlier set of data points supporting each identified regression class, and determine the a priori unknown number of statistically valid models in the data set. Although the models are extracted sequentially, the final result is almost independent of the extraction order due to a novel dynamic classification strategy employed in the handling of overlapping regression classes. The effectiveness and robustness of the RCMD method are substantiated by a set of simulation experiments and a real-life application showing the way it can be used to fit mixed data to linear regression classes and nonlinear structures in various situations.</p>
Data mining, genetic algorithm, maximum likelihood method, mixture modeling, RCMD method, regression class, robustness.

Y. Leung, J. Ma and W. Zhang, "A New Method for Mining Regression Classes in Large Data Sets," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 23, no. , pp. 5-21, 2001.
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