Name
Abstract | ||
|---|---|---|
The existence of missing entries in microarray data is problematic for the proper clustering process. Several approaches have been introduced to overcome this problem. The main idea of those methods is the inclusion of imputation step during clustering analysis. However, these approaches are usually computationally expensive and badly imputed values can possibly mislead clustering results. In this work, we present a new clustering method which combines the separate clustering results of individual sample dimensions without the imputation of missing values. The performance of our method was superior to other typical clustering methods when it was tested with one model dataset and four microarray datasets. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/FBIT.2007.135 | FBIT |
Keywords | Field | DocType |
microarray datasets,separate clustering result,proper clustering process,direct clustering method,imputation step,mislead clustering result,clustering analysis,typical clustering method,imperfect microarray data,missing entry,new clustering method,microarray data,cluster analysis,dna,missing values,imputation | Canopy clustering algorithm,Data mining,CURE data clustering algorithm,Clustering high-dimensional data,Correlation clustering,Computer science,Consensus clustering,Imputation (statistics),Biclustering,Cluster analysis | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taegyun Yun | 1 | 25 | 1.36 |
| Suyoung Kim | 2 | 0 | 0.34 |
| Taeho Hwang | 3 | 35 | 4.12 |
| Gwan-Su Yi | 4 | 111 | 10.72 |