Name
Title | ||
|---|---|---|
Non Negative Matrix Factorisation clustering capabilities; application on multivariate image segmentation |
Abstract | ||
|---|---|---|
The clustering capabilities of the Non Negative Matrix Factorisation (NMF) algorithm is studied. The basis images are considered like the membership degree of the data to a particular class. A hard clustering algorithm is easily derived based on these images. This algorithm is applied on a multivariate image to perform image segmentation. The results are compared with those obtained by Fuzzy K-means algorithm and better clustering performances are found for NMF based clustering. We also show that NMF performs well when we deal with uncorrelated clusters but it cannot distinguish correlated clusters. This is an important drawback when we try to use NMF to perform data clustering. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1504/IJBIDM.2010.033363 | CISIS |
Keywords | DocType | Volume |
multivariate image segmentation,important drawback,fuzzy k-means algorithm,negative matrix factorisation,basis image,hard clustering algorithm,correlated cluster,clustering performance,clustering capability,multivariate image,image segmentation,blind source separation,competitive intelligence,statistical analysis,application software,non negative matrix factorization,data analysis,data clustering,covariance matrix,decorrelation,indexing terms,software systems,clustering algorithms,distributed databases,fuzzy set theory,histogram,data mining,principal component analysis,probability density function | Journal | 5 |
Issue | Citations | PageRank |
3 | 5 | 0.44 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cosmin Lazar | 1 | 171 | 9.89 |
| Doncescu, A. | 2 | 86 | 25.70 |
| Nabil Kabbaj | 3 | 18 | 4.42 |