Ensembles based on random projections to improve the accuracy of clustering algorithms

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

Volumn 3931 LNCS, Issue , 2006, Pages 31-37

  Bertoni, Alberto ^a   Valentini, Giorgio ^a  

^a UNIVERSITY OF MILAN   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SCIENCE; DATABASE SYSTEMS; PROBLEM SOLVING;

CLUSTERING PROBLEMS; HIGH DIMENSIONAL DATA; METRIC SPACES; RANDOM PROJECTIONS;

NEURAL NETWORKS;

EID: 33745804068     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11731177_5     Document Type: Conference Paper

References (11)

1. Dietterich, T.: Ensemble methods in machine learning. In Kittler, J., Roli, F., eds.: Multiple Classifier Systems. First International Workshop, MCS 2000, Cagliari, Italy. Volume 1857 of Lecture Notes in Computer Science., Springer-Verlag (2000) 1-15
2. Valentini, G., Masulli, F.: Ensembles of learning machines. In: Neural Nets WIRN02. Volume 2486 of Lecture Notes in Computer Science. Springer-Verlag (2002) 3-19
3. Strehl, A., Ghosh, J.: Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions. Journal of Machine Learning Research 3 (2002) 583-618
4. Hadjitodorov, S., Kuncheva, L., Todorova, L.: Moderate Diversity for Better Cluster Ensembles. Information Fusion (2005)
5. Bertoni, A., Valentini, G.: Random projections for assessing gene expression cluster stability. In: IJCNN 2005, The IEEE-INNS International Joint Conference on Neural Networks, Montreal (2005) (in press).
6. Smolkin, M., Gosh, D.: Cluster stability scores for microarray data in cancer studies. BMC Bioinformatics 4 (2003)
7. Ho, T.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (1998) 832-844
8. Johnson, W., Lindenstrauss, J.: Extensions of Lipshitz mapping into Hubert space. In: Conference in modern analysis and probability. Volume 26 of Contemporary Mathematics., Amer. Math. Soc. (1984) 189-206
9. Bingham, E., Mannila, H.: Random projection in dimensionality reduction: Applications to image and text data. In: Proc. of KDD 01, San Francisco, CA, USA, ACM (2001)
10. Ward, J.: Hierarchcal grouping to optimize an objective function. J. Am. Stat. Assoc. 58 (1963) 236-244
11. Valentini, G.: An experimental bias-variance analysis of SVM ensembles based on resampling techniques. IEEE Transactions on Systems, Man and CyberneticsPart B: Cybernetics 35 (2005)