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Data-Centric Systems and Applications
W e study clustering under the data stream model of computation where: given a sequence of points, the objective is to maintain a consistently good clustering of the sequence observed so far, using a small amount of memory and time. The data stream model is relevant to new classes of applications involving massive data sets, such as web click stream analysis and multimedia data analysis. W e give constant-factor approximation algorithms for the k-Median problem in the data stream model ofdoi:10.1007/978-3-540-28608-0_8 fatcat:lpuw7ccbkfdspl62qyhmupi52y