Name
Title | ||
|---|---|---|
Cluster structure inference based on clustering stability with applications to microarray data analysis |
Abstract | ||
|---|---|---|
This paper focuses on the stability-based approach for estimating the number of clusters K in microarray data. The cluster stability approach amounts to performing clustering successively over random subsets of the available data and evaluating an index which expresses the similarity of the successive partitions obtained. We present a method for automatically estimating K by starting from the distribution of the similarity index. We investigate how the selection of the hierarchical clustering (HC) method, respectively, the similarity index, influences the estimation accuracy. The paper introduces a new similarity index based on a partition distance. The performance of the new index and that of other well-known indices are experimentally evaluated by comparing the "true" data partition with the partition obtained at each level of an HC tree. A case study is conducted with a publicly available Leukemia dataset. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1155/S1110865704309078 | EURASIP J. Adv. Sig. Proc. |
Keywords | Field | DocType |
similarity index,well-known index,hc tree,microarray data,data analysis,new index,new similarity index,available data,cluster structure inference,partition distance,data partition,clustering stability,successive partition,microarray data analysis | Hierarchical clustering,Fuzzy clustering,Data mining,Clustering high-dimensional data,Correlation clustering,Similarity (network science),Consensus clustering,Cluster analysis,Mathematics,Single-linkage clustering | Journal |
Volume | Issue | ISSN |
2004, | 1 | 1687-6180 |
Citations | PageRank | References |
7 | 0.58 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ciprian Doru Giurcaneanu | 1 | 43 | 12.44 |
| Ioan Tabus | 2 | 276 | 38.23 |