Name
Abstract | ||
|---|---|---|
Clustering techniques, such as hierarchical clustering, k- means algorithm and self-organizing maps, are widely used to analyze gene expression data. Results of these algorithms depend on several parameters, e.g., the number of clusters. However, there is no theoretical criterion to determine such parameters. In order to overcome this problem, we propose a method using mixture of PCA models trained by a variational Bayes (VB) estimation. In our method, good clustering results are selected based on the free energy obtained within the VB estimation. Furthermore, by taking an ensemble of estimation results, a robust clustering is achieved without any biological knowledge. Our method is applied to a clustering problem for gene expression data during a sporulation of Bacillus subtilis and it is able to capture characteristics of the sigma cascade. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-46084-5_85 | ICANN |
Keywords | Field | DocType |
clustering technique,robust clustering,estimation result,bacillus subtilis,good clustering result,vb estimation,pca models,gene expression data,pca model,hierarchical clustering,clustering problem,k means algorithm,free energy | Hierarchical clustering,Canopy clustering algorithm,Fuzzy clustering,Clustering high-dimensional data,CURE data clustering algorithm,Pattern recognition,Correlation clustering,Computer science,Determining the number of clusters in a data set,Artificial intelligence,Cluster analysis,Machine learning | Conference |
Volume | ISSN | ISBN |
2415 | 0302-9743 | 3-540-44074-7 |
Citations | PageRank | References |
2 | 0.40 | 4 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taku Yoshioka | 1 | 117 | 9.52 |
| Ryouko Morioka | 2 | 2 | 0.40 |
| Kazuo Kobayashi | 3 | 2 | 0.40 |
| Shigeyuki Oba | 4 | 290 | 27.68 |
| Naotake Ogawsawara | 5 | 2 | 0.40 |
| Shin Ishii | 6 | 532 | 43.99 |