Name
Abstract | ||
|---|---|---|
This paper discusses a density based clustering approach for a guided kernel based clustering algorithm, named MK-means (Modified K-means). Our idea is to improve the guided K-Means clustering algorithm and discuss the benefits of using MK-Means algorithm for clustering algorithm in astrophysics data bases. The improvements made allow handling clustering without apriori knowledge and also include the flexibility of merging classes when similarities are detected. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IJCNN.2010.5596300 | Neural Networks |
Keywords | Field | DocType |
astronomy computing,pattern clustering,astrophysics data bases,density based clustering approach,guided kernel based clustering algorithm,modified K-means clustering algorithm | Fuzzy clustering,Data mining,CURE data clustering algorithm,Computer science,Artificial intelligence,Cluster analysis,Single-linkage clustering,Canopy clustering algorithm,Data stream clustering,Pattern recognition,Correlation clustering,Determining the number of clusters in a data set,Machine learning | Conference |
ISSN | ISBN | Citations |
1098-7576 | 978-1-4244-6916-1 | 0 |
PageRank | References | Authors |
0.34 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hesam Dashti | 1 | 0 | 0.34 |
| Simas, T. | 2 | 0 | 0.34 |
| R. A. Ribeiro | 3 | 0 | 0.34 |
| Assadi, A. | 4 | 0 | 0.34 |
| A. Moitinho | 5 | 0 | 0.34 |