Name
Abstract | ||
|---|---|---|
We present the use of KICA to perform clustering of gene expression data. Comparison experiments between KICA and two other methods, PCA and ICA, are performed. Three clustering algorithms, including weighted graph partitioning, k-means and agglomerative hierarchical clustering, and two similarity measures, including Euclidean and Pearson correlation, are also evaluated. The results indicate that KICA is an efficient feature extraction approach for gene expression data clustering. Our empirical study showed that clustering with the components instead of the original variables does improve cluster quality. In particular, the first few components by KICA capture most of the cluster structure. We also showed that clustering with components has different impact on different algorithms and different similarity metrics. Overall, we would recommend KICA before clustering gene expression data. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11679363_57 | ICA |
Keywords | Field | DocType |
cluster quality,different algorithm,cluster structure,clustering gene expression data,gene expression data,different similarity metrics,similarity measure,agglomerative hierarchical clustering,clustering algorithm,different impact,kernel independent component analysis,empirical study,feature extraction,k means,independent component analysis | Fuzzy clustering,CURE data clustering algorithm,Computer science,Consensus clustering,Artificial intelligence,Biclustering,Cluster analysis,Single-linkage clustering,Discrete mathematics,Clustering high-dimensional data,Pattern recognition,Correlation clustering,Algorithm | Conference |
Volume | ISSN | ISBN |
3889 | 0302-9743 | 3-540-32630-8 |
Citations | PageRank | References |
1 | 0.35 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Jin | 1 | 333 | 62.83 |
| Anbang Xu | 2 | 351 | 30.52 |
| Rongfang Bie | 3 | 547 | 68.23 |
| Ping Guo | 4 | 601 | 85.05 |