Abstract
Biological data set sizes have been growing rapidly with the technological advances that have occurred in bioinformatics. Data mining techniques have been used extensively as approaches to detect interesting patterns in large databases. In bioinformatics, clustering algorithm technique for data mining can be applied to find underlying genetic and biological interactions, without considering prior information from datasets. However, many clustering algorithms are practically available, and different clustering algorithms may generate dissimilar clustering results due to bio-data characteristics and experimental assumptions. In this paper, we propose a novel heterogeneous clustering ensemble scheme that uses a genetic algorithm to generate high quality and robust clustering results with characteristics of bio-data. The proposed method combines results of various clustering algorithms and crossover operation of genetic algorithm, and is founded on the concept of using the evolutionary processes to select the most commonly-inherited characteristics. Our framework proved to be available on real data set and the optimal clustering results generated by means of our proposed method are detailed in this paper. Experimental results demonstrate that the proposed method yields better clustering results than applying a single best clustering algorithm.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Alexander, P.T., Behrouz, M.-B., Anil, K.J., William, F.P.: Adaptive clustering ensembles. In: Proceedings of the International Conference on Pattern Recognition, vol. 1, pp. 272–275 (2004)
Banerjee, A., Krumpelman, C., Basu, S., Mooney, R., Ghosh, J.: Model-based overlapping clustering. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining, pp. 532–537 (2005)
Greene, D., Tsymbal, A., Bolshakova, N., Cunningham, P.: Ensemble clustering in medical diagnostics. In: Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems, pp. 576–581 (2004)
Jaewoo, K., Jiong, Y., Wanhong, X., Pankaj, C.: Integrating heterogeneous microarray data sources using correlation signatures. In: Ludäscher, B., Raschid, L. (eds.) DILS 2005. LNCS (LNBI), vol. 3615, pp. 105–120. Springer, Heidelberg (2005)
Jouve, P.E., Nicoloyannis, N.: A new method for combining partitions, applications for distributed clustering. In: Proceedings of the International Workshop on Parallel and Distributed Machine Learning and Data Mining (2003)
Kasturi, J., Acharya, R.: Clustering of diverse genomic data using information fusion. Bioinformatics 21, 423–429 (2005)
Kenneth, J.R., Suzanne, D.V., Ellen, B., William, C.R.: The economic impact of chronic fatigue syndrome. Cost Effectiveness and Resource Allocation, 2 (2004)
Liu, J.J., Cutler, G., Li, W., Pan, Z., Peng, S., Hoey, T., Chen, L., Ling, X.B.: Multiclass cancer classification and biomarker discovery using GA-based algorithms. Bioinformatics 21, 2691–2697 (2005)
Patrick, C.H.M., Keith, C.C.C.: Discovering clusters in gene expression data using evolutionary approach. In: Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence, pp. 459–466 (2003)
Qiu, P., Wang, Z.J., Liu, K.J.: Ensemble dependence model for classification and prediction of cancer and normal gene expression data. Bioinformatics 21, 3114–3121 (2005)
Whistler, T., Unger, E.R., Nisenbaum, R., Vernon, S.D.: Integration of gene expression, clinical, and epidemiologic data to characterize Chronic Fatigue Syndrome. Journal of Translational Medicine 1 (2003)
Xiaohua, H.: Integration of cluster ensemble and text summarization for gene. In: Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering, pp. 251–258 (2004)
Xiaohua, H., Illhoi, Y.: Cluster ensemble and its applications in gene expression. In: Proceedings of the Asia-Pacific Bioinformatics Conference, vol. 29, pp. 297–302 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yoon, HS., Ahn, SY., Lee, SH., Cho, SB., Kim, J.H. (2006). Heterogeneous Clustering Ensemble Method for Combining Different Cluster Results. In: Li, J., Yang, Q., Tan, AH. (eds) Data Mining for Biomedical Applications. BioDM 2006. Lecture Notes in Computer Science(), vol 3916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11691730_9
Download citation
DOI: https://doi.org/10.1007/11691730_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33104-9
Online ISBN: 978-3-540-33105-6
eBook Packages: Computer ScienceComputer Science (R0)