Name
Abstract | ||
|---|---|---|
Tumor clustering from bio-molecular data provides a new way to perform cancer class discovery. In this paper, we propose a hybrid fuzzy cluster ensemble framework (HFCEF) for tumor clustering from cancer gene expression data. Compared with traditional cluster ensemble framework, HFCEF integrates both the hard clustering and the soft clustering into the cluster ensemble framework. Specifically, HFCEF first applies the affinity propagation algorithm (AP) to perform clustering on the attribute dimension, and generates a set of subspaces which are used to create a set of new datasets. Then, the fuzzy membership function and the affinity propagation algorithm are adopted to generate a set of fuzzy matrices in the ensemble. Finally, the normalized cut algorithm is served as the consensus function to summarize the set of fuzzy matrices and obtain the final result. The experiments on cancer gene expression profiles shows that the proposed framework works well on bio-molecular data, and provides more robust, stable and accurate results. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICCH.2012.6724479 | ICCH |
Keywords | DocType | Volume |
fuzzy set theory,pattern clustering,genetics,matrix algebra,cancer gene expression profiles,cancer gene expression data,hybrid cluster ensemble framework,soft clustering,fuzzy membership function,cancer,hybrid fuzzy cluster ensemble framework,fuzzy matrices,normalized cut algorithm,hard clustering,tumor clustering,cancer class discovery,hfcef,medical computing,bio-molecular data,attribute dimension,ap,affinity propagation algorithm,consensus function | Conference | null |
Issue | ISSN | ISBN |
null | null | 978-1-4673-5127-0 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiwen Yu | 1 | 8 | 2.48 |
| Jane You | 2 | 1885 | 136.93 |
| Hantao Chen | 3 | 36 | 2.93 |
| Le Li | 4 | 158 | 10.10 |
| Xiao-Wei Wang | 5 | 596 | 59.78 |