Discrete facility location in machine learning
Авторы: Vasilyev I., Ushakov A.V.
Журнал: Journal of Applied and Industrial Mathematics
Отчётный год: 2021
Аннотация: Facility location problems form a broad class of optimization problems extremely popularin combinatorial optimization and operations research. In every facility location problem, one mustlocate a set of facilities in order to satisfy the demands of customers so as some certain objectivefunction be optimal. Besides numerous applications in public and private sectors, the problemsare widely used in machine learning. For example, clustering can be viewed as a facility locationproblem where we need to partition a set of customers into clusters assigned to open facilities.In this survey we briefly look at how the ideas and approaches arisen in the field of facilitylocation led to modern, popular machine learning algorithms supported by many data mining andmachine learning software packages. We also review the state-of-the-art of exact methods andheuristics, as well as some extensions of the basic problems and algorithms in applied machinelearning tasks. Note that the main emphasis lies here on discrete facility location problems which,for example, underlie many widely used clustering algorithms (PAM, affinity propagation, etc.).Since the high computational complexity of the conventional facility location-based clusteringalgorithms hinders their application to modern large-scale real-life datasets; we also survey somemodern approaches to implementation of the algorithms for these large data collections.
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