Traditional clustering partitions a group of objects into a number of nonoverlapping sets based on a similarity measure. In real world, the boundaries of these sets or clusters may not be clearly defined. Some of the objects may be almost equidistant from the center of multiple clusters. Traditional set theory mandates that these objects be assigned to a single cluster. Rough set theory can be used to represent the overlapping clusters. Rough sets provide more flexible representation than conventional sets, at the same time they are less descriptive than the fuzzy sets. This paper describes the basic concept of rough clustering based on k-means, genetic algorithms, Kohonen self-organizing maps, and support vector clustering. The discussion also includes a review of rough cluster validity measures, and applications of rough clustering to such diverse areas as forestry, medicine, medical imaging, web mining, super markets, and traffic engineering. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 64-72 DOI: 10.1002/widm.16