Abstract
Bi-clustering is one of the main tasks in data mining with many possible applications in bioinformatics, pattern recognition, text mining, just to cite a few. It refers to simultaneously partitioning a data matrix based on both rows and columns. One of the main issues in bi-clustering is the difficulty to find the number of bi-clusters, which is usually pre-specified by the human user. During the last decade, a new algorithm, called MOCK, has appeared and shown its performance in data clustering where the number of clusters is determined automatically. Motivated by the interesting results of MOCK, we propose in this paper a new algorithm, called Bi-MOCK, which could be seen as an extension of MOCK for bi-clustering. Like MOCK, Bi-MOCK uses the concept of multi-objective optimization and is able to find automatically the number of bi-clusters thanks to a newly proposed variable string length encoding scheme. The performance of our proposed algorithm is assessed on a set of real gene expression datasets. The comparative experiments show the merits and the outperformance of Bi-MOCK with respect to some existing recent works.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans. Comput. Biol. Bioinform. 1(1), 24–45 (2004)
Kasim, A., Shkedy, Z., Kaiser, S., Hochreiter, S., Talloen, W.: Applied biclustering methods for big and high dimensional data using R (2016). ISBN 9781482208238
Freitas, A.V., Ayadi, W., Elloumi, M., Oliveira, J., Oliveira, J., Hao, J.-K.: A survey on biclustering of gene expression data. In: Elloumi, M., Zomaya, A.Y. (eds.) Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data. Wiley, Hoboken (2013). doi:10.1002/9781118617151.ch25
Handl, J., Knowles, J.D.: An evolutionary approach to multiobjective clustering. IEEE Trans. Evol. Comput. 11(1), 56–76 (2007)
Mitra, S., Banka, H.: Multi-objective evolutionary biclustering of gene expression data. Pattern Recogn. 39(12), 2464–2477 (2006)
Handl, J., Knowles, J.: Exploiting the trade-off—the benefits of multiple objectives in data clustering. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 547–560. Springer, Heidelberg (2005). doi:10.1007/978-3-540-31880-4_38
Bechikh, S., Ben Said, L., Ghedira, K.: Negotiating decision makers’ reference points for group preference-based evolutionary multi-objective optimization. In: International Conference on Hybrid Intelligent Systems, pp. 377–382 (2011)
Cheng, Y., Church, G.M.: Biclustering of expression data. In: International Conference on Intelligent Systems for Molecular Biology, pp. 93–103 (2000)
Yeast and Human datasets. http://arep.med.harvard.edu/biclustering/
Colon data set. http://genomics-pubs.princeton.edu/oncology/affydata/index.html
Seridi, K., Jourdan, L., Talbi, E.G.: Using multiobjective optimization for biclustering microarray data. Appl. Soft Comput. 33(1), 239–249 (2015)
Divina, F., Aguilar-Ruiz, J.S.: A multi-objective approach to discover biclusters in microarray data. In: International Conference on Genetic and Evolutionary Computation (GECCO 2007), pp. 385–392 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Bousselmi, M., Bechikh, S., Hung, CC., Said, L.B. (2017). Bi-MOCK: A Multi-objective Evolutionary Algorithm for Bi-clustering with Automatic Determination of the Number of Bi-clusters. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-70093-9_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70092-2
Online ISBN: 978-3-319-70093-9
eBook Packages: Computer ScienceComputer Science (R0)