Name
Abstract | ||
|---|---|---|
This paper introduces a new approach for clustering large datasets based on spectral clustering and topological unsupervised learning. Spectral clustering method needs to construct an adjacency matrix and calculate the eigen-decomposition of the corresponding Laplacian matrix [4] which are computational expensive and is not easy to apply on large-scale data sets. Contrarily, the topological learning i.e. SOM method allows a projection of the dataset in low dimensional spaces that make it easy to use for very large datasets. The prototypes matrix weighted by the neighbourhood function will be used in this work to reduce the computational time of the clustering algorithm and to add the topological information to the final clustering result. We illustrate the power of this method with several real datasets. The results show a good quality of clustering results and a higher speed. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-26535-3_25 | ICONIP |
Field | DocType | Volume |
Spectral clustering,Fuzzy clustering,Data mining,CURE data clustering algorithm,Computer science,Artificial intelligence,Cluster analysis,Canopy clustering algorithm,Topology,Clustering high-dimensional data,Data stream clustering,Pattern recognition,Correlation clustering,Machine learning | Conference | 9490 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicoleta Rogovschi | 1 | 40 | 8.42 |
| Nistor Grozavu | 2 | 67 | 16.76 |
| Lazhar Labiod | 3 | 34 | 13.50 |