Name
Abstract | ||
|---|---|---|
Clustering only the records in a database (or data matrix) gives a global view of the data. For a detailed analysis or a local view, biclustering or co-clustering is required, involving the clustering of the records and the attributes simultaneously. In this paper, a new graph-drawing-based biclustering technique is proposed based on the crossing minimization paradigm that is shown to work for asymmetric overlapping biclusters in the presence of noise. Both simulated and real world data sets are used to demonstrate the superior performance of the new technique compared with two other conventional biclustering approaches. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.neucom.2006.02.018 | Neurocomputing |
Keywords | Field | DocType |
Knowledge discovery,Data mining,Biclustering,Co-clustering,Graph drawing,Crossing minimization,Overlapping biclusters,Noise | Graph drawing,Data mining,Data set,Correlation clustering,Pattern recognition,Computer science,Minification,Knowledge extraction,Artificial intelligence,Biclustering,Cluster analysis,Machine learning | Journal |
Volume | Issue | ISSN |
69 | 16 | 0925-2312 |
Citations | PageRank | References |
25 | 1.61 | 17 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahsan Abdullah | 1 | 45 | 7.98 |
| Amir Hussain | 2 | 672 | 67.84 |