Back to Search Start Over

Adaptive Clustering via Symmetric Nonnegative Matrix Factorization of the Similarity Matrix

Authors :
Paola Favati
Grazia Lotti
Ornella Menchi
Francesco Romani
Source :
Algorithms, Vol 12, Iss 10, p 216 (2019)
Publication Year :
2019
Publisher :
MDPI AG, 2019.

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.

Details

Language :
English
ISSN :
19994893
Volume :
12
Issue :
10
Database :
Directory of Open Access Journals
Journal :
Algorithms
Publication Type :
Academic Journal
Accession number :
edsdoj.541b716c4e8b411abe07385c4082b430
Document Type :
article
Full Text :
https://doi.org/10.3390/a12100216