Sublinear clustering describes the process of clustering a given set of input objects using only a small subset of the input set, which is typically selected by a random process. A solution computed by a sublinear clustering algorithm is an implicit description of the clustering (rather than a partition of the input objects), for example in the form of cluster centers. Sublinear clustering is usually applied when the input set is too large to be processed with standard clustering algorithms.
Motivation and Background
Clusteringis the process of partitioning a set of objects into subsets of similar objects. In machine learning, it is, for example, used in unsupervised learning to fit input data to a density model. In many modern applications of clustering, the input sets consist of billions of objects to be clustered. Typical examples include web search, analysis of web traffic, and spam detection. Therefore, even though many relatively efficient clustering algorithms are...
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