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In many applications of clustering, solutions that are balanced, i.e, where the clusters obtained are of comparable sizes, are preferred. This chapter describes several approaches to obtaining balanced clustering results that also scale well to large data sets. First, we describe a general scalable framework for obtaining balanced clustering which first clusters only a small subset of the data and then efficiently allocates the rest of the data to these initial clusters while simultaneouslydoi:10.1201/9781584889977.ch8 fatcat:kj5gtm37ebbmtcvk3zw2dw2bde