Ensemble clustering with a fuzzy approach

Studies in Computational Intelligence

Volumn 126, Issue , 2008, Pages 49-69

  Avogadri, Roberto ^a   Valentini, Giorgio ^a  

^a UNIVERSITY OF MILAN   (Italy)

Author keywords

Ensemble clustering; Fuzzy sets; Gene expression; Johnson Lindenstrauss lemma; K means; Random projections; Triangular norm

Indexed keywords

EID: 42449100413     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-78981-9_3     Document Type: Conference Paper

References (23)

1. Fern XZ, Brodley CE (2003) Random projection for high dimensional data clustering: A cluster ensemble approach. In: Fawcett T, Mishra N (eds) Proc 20th Int Conf Mach Learning, Washington, DC, USA. AAAI Press, Menlo Park
2. Monti S, Tamayo P, Mesirov J, Golub T (2003) Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learning 52:91-118
3. Topchy A, Jain A, Puch W (2005) Clustering ensembles: Models of consensus and weak partitions. IEEE Trans Pattern Analysis Mach Intell 27:1866-1881
4. Kuncheva L, Vetrov D (2006) Evaluation of stability of k-means cluster ensembles with respect to random initialization. IEEE Trans Pattern Analysis Mach Intell 28:1798-1808
5. Kuncheva L (2004) Combining pattern classifiers: Methods and algorithms. Wiley-Interscience, New York
6. Strehl A, Ghosh J (2002) Cluster ensembles - a knowledge reuse framework for combining multiple partitions. J Mach Learn Research 3:583-618
7. Hu X, Yoo I (2004) Cluster ensemble and its applications in gene expression analysis. In: Chen YPP (ed) Proc. 2nd Asia-Pacific Bioinformatics Conf, Dunedin, New-Zealand. Australian Computer Society, Darlinghurst, pp 297-302
8. Dudoit S, Fridlyand J (2003) Bagging to improve the accuracy of a clustering procedure. Bioinformatics 19:1090-1099
9. Bertoni A, Valentini G (2006) Ensembles based on random projections to improve the accuracy of clustering algorithms. In: Apolloni B, Marinaro M, Nicosia G, Tagliaferri R (eds) Proc 16th Italian Workshop Neural Nets, Vietri sul Mare, Italy. Springer, Berlin/Heidelberg, pp 31-37
10. Hadjitodorov S, Kuncheva L, Todorova L (2006) Moderate diversity for better cluster ensembles. Inf Fusion 7:264-275
11. Achlioptas D (2003) Database-friendly random projections: Johnson-Lindenstrauss with binary coins. J Comp Sys Sci 66:671-687
12. Johnson WB, Lindenstrauss J (1984) Extensions of Lipshitz mapping into Hilbert space. Contemporary Math 26:189-206
13. Bertoni A, Valentini G (2006) Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses. Artif Intell Medicine 37:85-109
14. Eisen M, Spellman P, Brown P, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. PNAS 95:14863-14868
15. Zadeh, L (1965) Fuzzy sets. Inf Control 8:338-353
16. Klement EP, Mesiar R, Pap E (2000) Triangular norms. Kluwer Academic Publishers, Dordrecht
17. Yang L, Lv H, Wang W (2006) Soft cluster ensemble based on fuzzy similarity measure. In: IMACS Multiconf Comp Eng Systems Appl, Beijing, China, pp 1994-1997
18. Golub T, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES (1999) Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286:531-537
19. Bittner M, Meltzer P, Chen Y, Jiang Y, Seftor E, Hendrix M, Radmacher M, Simon R, Yakhini Z, Ben-Dor A, Sampas N, Dougherty E, Wang E, Marincola F, Gooden C, Lueders J, Glatfelter A, Pollock P, Carpten J, Gillanders E, Leja D, Dietrich K, Beaudry C, Berens M, Alberts D, Sondak V (2000) Molecular classification of malignant melanoma by gene expression profiling. Nature 406:536-540
20. Dietterich T (2000) An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization. Mach Learn 40:139-158
21. Avogadri R, Valentini G (2007) Fuzzy ensemble clustering for DNA microarray data analysis. In: Proc 4th Int Conf Bioinformatics and Biostatistics, Portofino, Italy. Springer, Berlin/Heidelberg, pp 537-543
22. Valentini G (2006) Clusterv: A tool for assessing the reliability of clusters discovered in DNA microarray data. Bioinformatics 22:369-370
23. Bertoni A, Valentini G (2007) Model order selection for bio-molecular data clustering. BMC Bioinformatics 8:S7