Robust clustering by aggregation and intersection methods

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

Volumn 5179 LNAI, Issue PART 3, 2008, Pages 732-739

  Bifulco, Ida ^a   Fedullo, Carmine ^a   Napolitano, Francesco ^a   Raiconi, Giancarlo ^a   Tagliaferri, Roberto ^a  

^a UNIVERSITY OF SALERNO   (Italy)

Author keywords

Consensus clustering; Dendrogram visualization; mds visualization; Meta clustering

Indexed keywords

CLUSTER ANALYSIS; VISUALIZATION;

AGGLOMERATIVE APPROACH; CLUSTERING SOLUTIONS; CONSENSUS CLUSTERING; DENDROGRAMS; INTERACTIVE VISUALIZATIONS; INTERSECTION METHOD; META CLUSTERING; MULTIPLE CLUSTERINGS;

KNOWLEDGE BASED SYSTEMS;

EID: 57749206390     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85567-5_91     Document Type: Conference Paper

References (21)

1. Berman, P., DasGupta, B., Kao, M., Wang, J.: On constructing an optimal consensus clustering from multiple clusterings. Inf. Process. Lett. 104, (4) 137-145 (2007)
2. Gusfield, D.: Partition-distance: A problem and class of perfect graphs arising in clustering. Information Processing Letters 82, 159-164 (2002)
3. Amato, R., Ciaramella, A., Deniskina, N., et al.: A Multi-Step Approach to Time Series Analysis and Gene Expression Clustering. Bioinformatics 22, 589-596 (1995)
4. Jiang, D., Tang, C., Zhang, A.: Cluster Analysis for Gene Expression Data: A Survey. IEEE Transactions on Knowledge and Data Engineering 16, (11) 1370-1386 (2004)
5. Xui, R., Wunsch, D.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645-678 (2005)
6. Hu, Y., Hu, Y.P.: Global optimization in clustering using hyperbolic cross points. Pattern Recognition 40(6), 1722-1733 (2007)
7. Agarwal, P.K., Mustafa, N.H.: k-means projective clustering. In: Proceedings of the Twenty-Third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 155-165. ACM Press, New York (2004)
8. Kaukoranta, T., Franti, P., Nevalainen, O.: Reallocation of GLA codevectors for evading local minima. Electronics Letters 32(17), 1563-1564 (1996)
9. Valentini, G., RufEno, F.: Characterization Of Lung Tumor Subtypes Through Gene Expression Cluster Validity Assessment. RAIRO-Inf. Theor. Appl. 40, 163-176 (2006)
10. Bertoni, A., Valentini, G.: Random projections for assessing gene expression cluster stability. In: Proceedings IEEE International Joint Conference on Neural Networks, vol. 1, pp. 149-154 (2005)
11. Kuncheva, L.I., Vetrov, D.P.: Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization. PAMI 28(11), 1798-1808 (2006)
12. Caruana, R., Elhawary, M., Nguyen, N., Smith, C.: Meta Clustering. In: ICDM, pp. 107-118 (2006)
13. Bifulco, I., Murino, L., Napolitano, F., Raiconi, G., Tagliaferri, R.: Using Global Optimization to Explore Multiple Solutions of Clustering Problems. In: KES 2008 (2008)
14. Ciaramella, A., Cocozza, S., Iorio, F., Miele, G., Napolitano, F., Pinelli, M., Raiconi, G., Tagliaferri, R.: Interactive data analysis and clustering of genomic data. Neural Networks 21, 368-378 (2007)
15. Napolitano, F., Raiconi, G., Tagliaferri, R., Ciaramella, A., Staiano, A., Miele, A.: Clustering and visualization approaches for human cell cycle gene expression data analysis. International Journal Of Approximate Reasoning 47(1), 70-84 (2008)
16. Barthélemy, J.P., Leclerc, B.: The median procedure for partitions. In: Cox, I.J., Hansen, P., Julesz, B. (eds.) Partitioning Data Sets, American Mathematical Society, Providence, RI, pp. 3-34 (1995)
17. Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. ACM Trans. Knowl. Discov. Data 1(4), 1 (2007)
18. Bertolacci, M., Wirth, A.: Are approximation algorithms for consensus clustering worthwhile? In: 7th SIAM International Conference on Data Mining, pp. 437-442 (2007)
19. Nguyen, N., Caruana, R.: Consensus Clustering. In: Perner, P. (ed.) ICDM 2007. LNCS (LNAI), vol. 4597, pp. 607-612. Springer, Heidelberg (2007)
20. Topchy, A., Jain, A.K., Punch, W.: Clustering ensembles: models of consensus and weak partitions. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(12), 1866-1881 (2005)
21. Topchy, A., Minaei-Bidgoli, B., Jain, A.K., Punch, W.F.: Adaptive clustering ensembles. Pattern Recognition. In: Proceedings of the 17th International Conference, vol. 1, pp. 272-275 (2004)