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Adaptive Clustering via Symmetric Nonnegative Matrix Factorization of the Similarity Matrix

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Istituto di Informatica e Telematica-Consiglio Nazionale delle Ricerche (IIT-CNR), Via G. Moruzzi 1, 56124 Pisa, Italy
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Dipartimento di Matematica, University of Parma, Parco Area delle Scienze 53/A, 43124 Parma, Italy
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Dipartimento di Informatica, University of Pisa, Largo Pontecorvo 3, 56127 Pisa, Italy
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(10), 216; https://doi.org/10.3390/a12100216
Received: 12 September 2019 / Revised: 7 October 2019 / Accepted: 15 October 2019 / Published: 17 October 2019
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. View Full-Text
Keywords: clustering; nonnegative matrix factorization; adaptive strategy clustering; nonnegative matrix factorization; adaptive strategy
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MDPI and ACS Style

Favati, P.; Lotti, G.; Menchi, O.; Romani, F. Adaptive Clustering via Symmetric Nonnegative Matrix Factorization of the Similarity Matrix. Algorithms 2019, 12, 216.

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