An external cluster validation index, adjusted rand index (C-rand), and two internal cluster validation indices; silhouette, and modularity are used for comparison purposes.
📌 3 Key Takeaways
🎯
Objective & Hypothesis:Ensemble clustering is a promising approach that combines the results of multiple clustering algorithms to obtain a consensus partition by merging different partitions based upon well-defined rules.
📊
Key Findings & Data:In this study, we use an ensemble clustering approach for merging the results of five different clustering algorithms that are sometimes used in bioinformatics applications.
💡
Conclusion & Impact:An external cluster validation index, adjusted rand index (C-rand), and two internal cluster validation indices; silhouette, and modularity are used for comparison purposes.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
Ensemble clustering is a promising approach that combines the results of multiple clustering algorithms to obtain a consensus partition by merging different partitions based upon well-defined rules. In this study, we use an ensemble clustering approach for merging the results of five different clustering algorithms that are sometimes used in bioinformatics applications. The ensemble clustering result is tested on microar-ray data sets and compared with the results of the individual algorithms. An external cluster validation index, adjusted rand index (C-rand), and two internal cluster validation indices; silhouette, and modularity are used for comparison purposes.
Özet
Türkçe Öz Yok
🔖
Cite This Article
PİRİM, H., GAUTAM, D., BHOWMIK, T., PERKINS, A. D., EKŞİOĞLU, B., ALKAN, A. (2011). Performance of an ensemble clustering algorithm on biological data sets. Mathematical and Computational Applications, Cilt: 16 - Sayı: 1, 87–96. https://ajindex.com/articles/280503
Hu, X. and Yoo, I., Cluster ensemble and its applications in gene expression analysis. In 2nd Asia-Pacific Bioinformatics Conference. Dunedin, New Zealand. 29. Australian Computer Society, 2004. 2.Fred, A. L. N.; Jain, A. K. Combining multiple clusterings using evidence accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 27 (6): 835-849, 2005. 3.Nascimento, M. C. V.; Toledo, F. M. B.; Carvalho, A. C. P. L. F., Investigation of a new GRASP-based clustering algorithm applied to biological data. Computers and Operations Research. 37: 1381-1387, 2010. 4.Yeung, K. Y.; Fraley, C.; Murua, A.; Raftery, A. E. and Ruzzo, W. L., Model-based clustering and data trasformations for gene expression data. Bioinformatics. 17:977-987,2001. 5.Pirim, H.; Gautam, D.; Bhowmik, T.; Perkins, A. D. and Eksioglu, B., Performance evaluation of a community structure finding algorithm using modularity and C-rand measures. In IEEE International Joint Conference on Neural Networks, Barcelona, Spain, 18-23 July, 2010. 6.Rousseeuw, P. J., Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics. 20: 53-65, 1987. 7.Newman, M. E. J. and Girvan, M. Finding and evaluating community structure in networks, Physical Review E. 69, 2004. 8.Hubert, L. and Arabie, P., Comparing partitions. Journal of Classification. 2: 193-218,1985. 9.Asur, S.; Parthasarathy, S. and Ucar, D., An ensemble approach for clustering scale-free graphs. In LinkKDD. Philadelphia, PA, 20 August. ACM, 2006. 10.Asur,S.; Ucar, D.; and Parthasarathy, S., An ensemble framework for clustering protein-protein interaction networks. Bioinformatics 23: i29-i40, 2007. 11.Watts, D. and Strogatz, S., Collective dynamics of small world networks. Nature. 393(6684): 440-442, 1998. 12.Wang, T., Comparing Hard and Fuzzy C-Means for Evidence-Accumulation Clustering, In FUZZ-IEEE. Korea, 20-24 August, 2009. 13.Bezdek, I. C., Pattern recognition with fuzzy objective function algorithms. New York: Plenum, 1981. 14.Avogadri, R.; and Valentini, G., Fuzzy ensemble clustering based on random projections for DNA microarray data analysis. Artificial Intelligence in Medicine 45: 173-183,2008. 15.Hore, P.; Hall, L. O. and Goldgof. D. B., A scalable framework for cluster ensembles. Pattern Recognition. 42: 676 - 688, 2009. 16.Galluccio, L.; Michel, O. J.J.; Comon, P.; Hero, Alfred O.; Kliger, M., Combining multiple partitions created with a graph-ba