Name
Abstract | ||
|---|---|---|
Biclustering involves the simultaneous clustering of objects and their attributes, thus defining local two-way clustering models. Recently, efficient algorithms were conceived to enumerate all biclusters in real-valued datasets. In this case, the solution composes a complete set of maximal and non-redundant biclusters. However, the ability to enumerate biclusters revealed a challenging scenario: in noisy datasets, each true bicluster may become highly fragmented and with a high degree of overlapping. It prevents a direct analysis of the obtained results. Aiming at reverting the fragmentation, we propose here two approaches for properly aggregating the whole set of enumerated biclusters: one based on single linkage and the other directly exploring the rate of overlapping. Both proposals were compared with each other and with the actual state-of-the-art in several experiments, and they not only significantly reduced the number of biclusters but also consistently increased the quality of the solution. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-21024-7_18 | Machine Learning and Data Mining in Pattern Recognition |
Keywords | Field | DocType |
Biclustering,Bicluster enumeration,Bicluster aggregation,Outlier removal,Metrics for biclusters | Outlier removal,Data mining,Pattern recognition,Computer science,Artificial intelligence,Biclustering,Cluster analysis,Machine learning,Single Linkage | Journal |
Volume | ISSN | Citations |
abs/1506.01077 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saullo Haniell Galvão de Oliveira | 1 | 1 | 0.35 |
| Rosana Veroneze | 2 | 6 | 2.51 |
| Fernando J. Von Zuben | 3 | 831 | 81.83 |