European Journal of Operational Research | Vol.262, Issue.1 | | Pages
Dominant-set clustering: A review
Clustering refers to the process of extracting maximally coherent groups from a set of objects using pairwise, or high-order, similarities. Traditional approaches to this problem are based on the idea of partitioning the input data into a predetermined number of classes, thereby obtaining the clusters as a by-product of the partitioning process. A radically different perspective of the problem consists in providing a formalization of the very notion of a cluster and considering the clustering process as a sequential search of structures in the data adhering to this cluster notion. In this manuscript we review one of the pioneering approaches falling in the latter class of algorithms, which has been proposed in the early 2000s and has been found since then a number of applications in different domains. It is known as dominant set clustering and provides a notion of a cluster (
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Dominant-set clustering: A review
Clustering refers to the process of extracting maximally coherent groups from a set of objects using pairwise, or high-order, similarities. Traditional approaches to this problem are based on the idea of partitioning the input data into a predetermined number of classes, thereby obtaining the clusters as a by-product of the partitioning process. A radically different perspective of the problem consists in providing a formalization of the very notion of a cluster and considering the clustering process as a sequential search of structures in the data adhering to this cluster notion. In this manuscript we review one of the pioneering approaches falling in the latter class of algorithms, which has been proposed in the early 2000s and has been found since then a number of applications in different domains. It is known as dominant set clustering and provides a notion of a cluster (
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