The problem of clustering, that is, the partitioning of data into groups of similar objects, is a key step for many data-mining problems. The algorithm we propose for clustering is based on the symmetric nonnegative matrix factorization (SymNMF) of a similarity matrix. The algorithm is first presented for the case of a prescribed number k of clusters, then it is extended to the case of a not a priori given k. A heuristic approach improving the standard multistart strategy is proposed and validated by the experimentation.

Adaptive clustering via symmetric nonnegative matrix factorization of the similarity matrix

Menchi O.
Co-primo
;
Romani F.
Co-primo
2019-01-01

Abstract

The problem of clustering, that is, the partitioning of data into groups of similar objects, is a key step for many data-mining problems. The algorithm we propose for clustering is based on the symmetric nonnegative matrix factorization (SymNMF) of a similarity matrix. The algorithm is first presented for the case of a prescribed number k of clusters, then it is extended to the case of a not a priori given k. A heuristic approach improving the standard multistart strategy is proposed and validated by the experimentation.
2019
Favati, P.; Lotti, G.; Menchi, O.; Romani, F.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1013650
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