Cluster identification and separation in the growing self-organizing map: Application in protein sequence classification

Neural Computing and Applications

Volumn 19, Issue 4, 2010, Pages 531-542

  Ahmad, Norashikin ^a   Alahakoon, Damminda ^a   Chau, Rowena ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

Cluster identification; Cluster separation; Dynamic self organizing map; Protein sequence classification; Unsupervised neural networks

Indexed keywords

EID: 77952876529     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-009-0300-0     Document Type: Article

References (28)

1. Kohonen T (1990) The self-organizing map. Proc IEEE 78: 1464-1480.
2. Fritzke B (1994) Growing cell structures: a self-organizing network for unsupervised and supervised learning. Neural Netw 7: 1441-1460.
3. Blackmore J, Miikkulainen R (1993) Incremental grid growing: encoding high-dimensional structure into a two-dimensional feature map. In: IEEE international conference on neural networks, pp 450-455.
4. Alahakoon LD (2000) Data mining with structure adapting neural networks. In: School of computer science and software engineering. Monash University, pp xvii, 286 leaves.
5. Alahakoon D, Halgamuge SK, Srinivasan B (2000) Dynamic self-organizing maps with controlled growth for knowledge discovery. IEEE Trans Neural Netw 11: 601-614.
6. Alahakoon LD (2004) Controlling the spread of dynamic self-organising maps. Neural Comput Appl 13: 168-174.
7. Amarasiri R, Alahakoon D, Smith KA (2004) HDGSOM: a modified growing self-organizing map for high dimensional data clustering. In: Fourth international conference on hybrid intelligent systems, 2004 (HIS '04), pp 216-221.
8. Zheng X, Liu W, He P, Dai W (2004) Document clustering algorithm based on tree-structured growing self-organizing feature map advances in neural networks-ISNN 2004, pp 840-845.
9. Hsu AL, Tang S-L, Halgamuge SK (2003) An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data. Bioinformatics 19: 2131-2140.
10. Chan C-KK, Hsu AL, Tang S-L, Halgamuge SK (2008) Using growing self-organising maps to improve the binning process in environmental whole-genome shotgun sequencing. J Biomed Biotechnol 2008: 10.
11. Wang H, Azuaje F, Black N (2004) An integrative and interactive framework for improving biomedical pattern discovery and visualization. IEEE Trans Inf Technol Biomed 8: 16-27.
12. Zheng H, Wang H, Azuaje F (2008) Improving pattern discovery and visualization of SAGE data through poisson-based self-adaptive neural networks. IEEE Trans Inf Technol Biomed 12: 459-469.
13. Wang H, Zheng H, Hu J (2008) Poisson approach to clustering analysis of regulatory sequences. Int J Comput Biol Drug Design 1: 141-157.
14. Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1: 224-227.
15. Amarasiri R, Wickramasinge K, Alahakoon D (2003) Enhanced cluster visualization using the data skeleton model. In: 3rd international conference on intelligent systems design and application (ISDA), Oklahoma, USA.
16. Hsu A, Alahakoon D, Halgamuge SK, Srinivasan B (2000) Automatic clustering and rule extraction using a dynamic SOM tree. In: Proceedings of the 6th international conference on automation, robotics, control and vision, Singapore.
17. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31: 264-323.
18. Vesanto J, Alhoniemi E (2000) Clustering of the self-organizing map. IEEE Trans Neural Netw 11: 586-600.
19. Boeckmann B, Bairoch A, Apweiler R, Blatter M-C, Estreicher A, Gasteiger E, Martin MJ, Michoud K, O'Donovan C, Phan I, Pilbout S, Schneider M (2003) The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucl Acids Res 31: 365-370.
20. Ferran EA, Pflugfelder B, Ferrara P (1994) Self-organized neural maps of human protein sequences. Protein Sci 3: 507-521.
21. Wang H-C, Dopazo J, De La Fraga LG, Zhu Y-P, Carazo JM (1998) Self-organizing tree-growing network for the classification of protein sequences. Protein Sci 7: 2613-2622.
22. Wu CH, McLarty JW (2000) Neural networks and genome informatics. Elsevier, Oxford, Amsterdam.
23. Li ZR, Lin HH, Han LY, Jiang L, Chen X, Chen YZ (2006) PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucl Acids Res 34: W32-37.
24. Andrade MA, Casari G, Sander C, Valencia A (1997) Classification of protein families and detection of the determinant residues with an improved self-organizing map. Biol Cybern 76: 441-450.
25. Ferran EA, Ferrara P (1991) Topological maps of protein sequences. Biol Cybern 65: 451-458.
26. Wu CH, Ermongkonchai A, Chang T-C (1991) Protein classification using a neural network database system. In: Proceedings of the conference on analysis of neural network applications. ACM, Fairfax, Virginia, United States.
27. Wu C, Whitson G, McLarty J, Ermongkonchai A, Chang TC (1992) Protein classification artificial neural system. Protein Sci 1: 667-677.
28. Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16: 645-678.