Abstract
MicroRNAs are a class of small RNA molecules, which play an important regulatory role for the gene expression of animals and plants. Various studies have proved that microRNAs tend to cluster on chromosomes. In this regard, a novel clustering algorithm is proposed in this paper, integrating rough hypercuboid approach and interval type-2 fuzzy c-means. Rough hypercuboid equivalence partition matrix is used here to compute the lower approximation and boundary region implicitly for the clusters without the need of any user-specified threshold. Interval-valued fuzzifier is used to deal with the uncertainty associated with the fuzzy clustering parameters. The effectiveness of proposed method, along with a comparison with existing clustering techniques, is demonstrated on several microRNA data sets using some widely used cluster validity indices.
This is a preview of subscription content, log in via an institution.
References
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs, NJ (1988)
Domany, E.: Cluster Analysis of gene expression data. J. Stat. Phys. 110(3–6), 1117–1139 (2003)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer, Dordrecht, The Netherlands (1991)
Maji, P., Garai, P.: Fuzzy-Rough simultaneous attribute selection and feature extraction algorithm. IEEE Trans. Syst. Man Cybern. Part B Cybern. 43(4), 1166–1177 (2013)
Maji, P., Garai, P.: Simultaneous feature selection and extraction using fuzzy rough sets. In: Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), pp. 115–123, Dec 2012
Maji, P., Pal, S.K.: Rough set based generalized fuzzy C-means algorithm and quantitative indices. IEEE Trans. Syst. Man Cybern. Part B Cybern. 37(6), 1529–1540 (2007)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning-1. Inf. Sci. 8, 199–249 (1975)
Rhee, F., Hwang, C.: A type-2 fuzzy C-means clustering algorithm. In: Joint 9th IFSA World Congress and 20th NAFIPS International Conference, vol. 4, pp. 1926–1929, July 2001
Hwang, C., Rhee, C.: Uncertain fuzzy clustering: interval type-2 fuzzy approach to C-means. IEEE Trans. Fuzzy Syst. 15(1), 107–120 (2007)
Maji, P., Garai, P.: IT2 fuzzy-rough sets and max relevance-max significance criterion for attribute selection. IEEE Trans. Cybern. 45(8), 1657–1668 (2015)
Maji, P.: Rough hypercuboid approach for feature selection in approximation spaces. IEEE Trans. Knowl. Data Eng. 26(1), 16–29 (2014)
Mendel, J.M., Karnik, N.N.: Centroid of a type-2 fuzzy set. Inf. Sci. 132(1), 195–220 (2001)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithm. Plenum Press, New York (1981)
Maji, P., Paul, S.: Robust rough-fuzzy C-means algorithm: design and applications in coding and non-coding RNA expression data clustering. Fundam. Inf. 124, 153–174 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Garai, P., Maji, P. (2018). Identification of Co-expressed microRNAs Using Rough Hypercuboid-Based Interval Type-2 Fuzzy C-Means Algorithm. In: Saeed, K., Chaki, N., Pati, B., Bakshi, S., Mohapatra, D. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 564. Springer, Singapore. https://doi.org/10.1007/978-981-10-6875-1_6
Download citation
DOI: https://doi.org/10.1007/978-981-10-6875-1_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6874-4
Online ISBN: 978-981-10-6875-1
eBook Packages: EngineeringEngineering (R0)