Name
Title | ||
|---|---|---|
Individual clustering and homogeneous cluster ensemble approaches applied to gene expression data |
Abstract | ||
|---|---|---|
Exploratory data analysis and, in particular, data clustering can significantly benefit from combining multiple data partitions – cluster ensemble. In this context, we analyze the potential of applying cluster ensemble techniques to gene expression microarray data. Our experimental results show that there is often a significant improvement in the results obtained with the use of ensemble techniques when compared to those based on the clustering techniques used individually. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11589990_113 | Australian Conference on Artificial Intelligence |
Keywords | Field | DocType |
individual clustering,exploratory data analysis,ensemble technique,gene expression microarray data,gene expression data,cluster ensemble,clustering technique,cluster ensemble technique,significant improvement,multiple data partition,homogeneous cluster ensemble,data clustering,microarray data,gene expression | Data mining,Clustering high-dimensional data,Computer science,Rand index,Consensus clustering,Microarray analysis techniques,Cluster analysis,Exploratory data analysis,DNA microarray,Single-linkage clustering | Conference |
Volume | ISSN | ISBN |
3809 | 0302-9743 | 3-540-30462-2 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shirlly C. M. Silva | 1 | 0 | 0.34 |
| Daniel S. A. De Araujo | 2 | 211 | 9.14 |
| Raul B. Paradeda | 3 | 2 | 1.06 |
| Valmar S. Severiano-Sobrinho | 4 | 0 | 0.34 |
| Marcilio C. P. de Souto | 5 | 106 | 10.47 |