We review the current state of data mining and machine learning in astronomy.'Data Mining' can have a somewhat mixed connotation from the point of view of aresearcher in this field. If used correctly, it can be a powerful approach,holding the potential to fully exploit the exponentially increasing amount ofavailable data, promising great scientific advance. However, if misused, it canbe little more than the black-box application of complex computing algorithmsthat may give little physical insight, and provide questionable results. Here,we give an overview of the entire data mining process, from data collectionthrough to the interpretation of results. We cover common machine learningalgorithms, such as artificial neural networks and support vector machines,applications from a broad range of astronomy, emphasizing those where datamining techniques directly resulted in improved science, and important currentand future directions, including probability density functions, parallelalgorithms, petascale computing, and the time domain. We conclude that, so longas one carefully selects an appropriate algorithm, and is guided by theastronomical problem at hand, data mining can be very much the powerful tool,and not the questionable black box.
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