Clustering and metaclustering with nonnegative matrix decompositions

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3720 LNAI, Issue , 2005, Pages 10-22

  Badea, Liviu ^a  

^a NATIONAL INSTITUTE FOR RESEARCH AND DEVELOPMENT IN INFORMATICS   (Romania)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; LEARNING SYSTEMS; PROBLEM SOLVING; STABILITY;

BICLUSTERING; CLUSTERING; MATRIX DECOMPOSITION; METACLUSTERING;

DATA MINING;

EID: 33646436706     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11564096_7     Document Type: Conference Paper

References (10)

1. Bradley P.S., Fayyad U.M. Refining Initial Points for K-Means Clustering, Proc. ICML-98, pp. 91-99.
2. Eisen M.B., P.T. Spellman, P.O. Brown, D. Botstein. Cluster analysis and display of genome-wide expression patterns, PNAS Vol.95, 14863-8, Dec. 1998.
3. Hoyer P.O. Non-negative sparse coding. Neural Networks for Signal Processing XII, 557-565, Martigny, 2002.
4. Lee D.D., H.S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, vol. 401, no. 6755, pp. 788-791, 1999.
5. Lee D.D., H.S. Seung. Algorithms for non-negative matrix factorization. Proc. NIPS*2000, MIT Press, 2001.
6. Welling M., Weber M. Positive tensor factorization. Pattern Recognition Letters 22(12): 1255-1261 (2001).
7. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, 1981.
8. Bauer E. Kohavi R. An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning 36 (1999) 105-139.
9. Cheng Y. Church G. Biclustering of expression data. Proc. ISMB-2000, 93-103.
10. Bhattacharjee et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc. Natl. Acad. Sci. USA. 2001 Nov. 20;98(24): 13790-5.