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Use Of Evolutionary Algorithms In Data Mining
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RESOURCES FOR EVOLUTIONARY ALGORITHMS IN DATA MINING
With evolutionary algorithms rapidly gaining acceptance in data mining, there are a variety of resources that the interested researcher can refer to for the most recent advances in the field. There are several conferences held in the various topics covered in this chapter, including the EvoIASP conferences organized by the Working Group on Evolutionary Algorithms in Image Analysis and Signal Processing (2001), Knowledge Discovery and Data Mining (KDD), International Conference on Machine Learning (ICML), and the Genetic and Evolutionary Computation Conference (GECCO). The journals Evolutionary Computation, Genetic Programming and Evolvable Machines, IEEE Transactions on Systems, Man, and Cybernetics, and the IEEE Transactions on Evolutionary Computation are also excellent resources. There are several resources available on the Internet as well. A comprehensive bibliography on genetic algorithms by Alander (2000) includes their use in classifier systems, image processing, signal processing, neural networks, etc.

ABSTRACT
With computers becoming more pervasive, disks becoming cheaper, and sensors becoming ubiquitous, we are collecting data at an ever-increasing pace. However, it is far easier to collect the data than to extract useful information from it. Sophisticated techniques, such as those developed in the multi-disciplinary field of data mining, are increasingly being applied to the analysis of these datasets in commercial and scientific domains. As the problems become larger and more complex, researchers are turning to heuristic techniques to complement existing approaches. This survey paper examines the role that evolutionary algorithms (EAs) can play in various stages of data mining. We consider data mining as the end-to-end process of finding patterns starting with raw data. The paper focuses on the topics of feature extraction, feature selection, classification, and clustering, and surveys the state of the art in the application of evolutionary algorithms to these areas. We examine the use of evolutionary algorithms both in isolation and in combination with other algorithms including neural networks, and decision trees. The paper concludes with a summary of open research problems and opportunities for the future.

Source: wiki.uelceca.net

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