Back to Search
Start Over
Adaptive Clustering via Symmetric Nonnegative Matrix Factorization of the Similarity Matrix
- 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