« Previous
Next »
Artificial Intelligence in Medicine
Volume 45, Issue 2
, Pages 173-183
, February 2009
Fuzzy ensemble clustering based on random projections for DNA microarray data analysis
References
- . Identifying distinct classes of bladder carcinoma using microarrays. Nature Genetics. 2002;33(December):90–96
- . Gene expression profiling in uveal melanoma reveals two molecular classes and predicts metastatic death. Cancer Research. 2004;64(October):7205–7209
- . Functional interpretation of microarray experiments. OMICS. 2006;3(September (10)):
- . Cluster ensembles—a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research. 2003;3(January):583–617
- . Random projections for high dimensional data clustering: a cluster ensemble approach. In: Fawcett T, Mishra N editor. Machine learning, proceedings of the twentieth international conference (ICML 2003). Washington, D.C., USA: AAAI Press; 2003;
- . Clustering ensembles: models of consensus and weak partitions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005;27(12):1866–1881
- . Evaluation of stability of k-means cluster ensembles with respect to random initialization. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2006;28(11):1798–1808
- . Consensus clustering and functional interpretation of gene-expression data. Genome Biology. 2004;5(11):94
- . Bagging to improve the accuracy of a clustering procedure. Bioinformatics. 2003;19(9):1090–1099
- . Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learning. 2003;52(1/2):91–118
- . Cluster ensemble and its applications in gene expression analysis. In: Proceedings of the 2nd Asia-Pacific bioinformatics conference. Dunedin, New-Zealand. Darlinghurst, Australia: Australian Computer Society Inc.; 2004;p. 297–302
- . Robust multi-scale clustering of large DNA microarray data sets with the consensus algorithm. Bioinformatics. 2006;22(1):58–67
- . Graph based consensus clustering for class discovery from gene expression data. Bioinformatics. 2007;23(21):2888–2896
- . Bagging predictors. Machine Learning. 1996;24(2):123–140
- . Ensembles based on random projections to improve the accuracy of clustering algorithms. In: Neural nets, WIRN 2005 vol. 3931 of lecture notes in computer science. Springer; 2006;p. 31–37
- Bertoni A, Valentini G. Randomized embedding cluster ensembles for gene expression data analysis. In: SETIT 2007—IEEE international conference on sciences of electronic, technologies of information and telecommunications. Hammamet, Tunisia; 2007.
- . Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses. Artificial Intelligence in Medicine. 2006;37(2):85–109
- . Model order selection for bio-molecular data clustering. BMC Bioinformatics. 2007;8(May (Suppl. 2)):
- . Exploring the conditional regulation of yeast gene expression through fuzzy k-means clustering. Genome Biology. 2002;3(11):1–22
- . Adaptive control processes: a guided tour. NJ: Princeton University Press; 1961;
- . Extensions of Lipshitz mapping into Hilbert space. Conference in modern analysis and probability, vol. 26 of Contemporary Mathematics. American Mathematical Society. 1984;189–206
- . Database-friendly random projections. In: Buneman P editors. Proceedings of the ACM symposium on the principles of database systems, contemporary mathematics. New York, NY, USA: ACM Press; 2001;p. 274–281
- . Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of the KDD 01. San Francisco, CA, USA: ACM; 2001;
- . Cluster stability scores for microarray data in cancer studies. BMC Bioinformatics. 2003;36(4):
- . The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1998;20(8):832–844
- . Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Medicine. 2002;8(1):68–74
- . A molecular signature of metastasis in primary solid tumors. Nature Genetics. 2003;33:49–54
- . Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286:531–537
- . Molecular classification of malignant melanoma by gene expression profiling. Nature. 2000;406:536–540
- . Hierarchical grouping to optimize an objective function. Journal of American Statistical Association. 1963;58(301):236–244
- . An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting and randomization. Machine Learning. 2000;40(2):139–158
- . Moderate diversity for better cluster ensembles. Information Fusion. 2006;7(3):264–275
- . Triangular norms. Kluwer Academic; 2000;
- . Database-friendly random projections: Johnson–Lindenstrauss with binary coins. Journal of Computer & System Sciences. 2003;66(4):671–687
- . Clusterv: a tool for assessing the reliability of clusters discovered in DNA microarray data. Bioinformatics. 2006;22(3):369–370
PII: S0933-3657(08)00101-2
doi: 10.1016/j.artmed.2008.07.014
© 2008 Elsevier B.V. All rights reserved.
« Previous
Next »
Artificial Intelligence in Medicine
Volume 45, Issue 2
, Pages 173-183
, February 2009
