Description:
Clustering problems arise in various contexts including classification, information retrieval and data mining. Roughly speaking, clustering refers to partitioning a set of objects into groups of similar objects. The objects (e.g. documents or images) are usually represented as points in some space with a distance measure and the objective is to obtain clusters of points that are close to each other. Problems of this flavor also occur in discrete location theory, where the goal is to locate a set of facilities (e.g. factories or warehouses) so as to serve a given set of clients. This talk describes several algorithmic aspects of clustering problems. Speaker(s):
Moses Charikar, Stanford University
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