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.File in questo prodotto:
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