Title: Fuzzy-soft-fuzzy set model for mining amino acid associations in peptide sequences of Mycobacterium tuberculosis complex (MTBC)

Authors: Amita Jain; Kamal Raj Pardasani

Addresses: A-1006, Vraj Green Valley, Kolshet Road, Thane 400607, Maharashtra, India ' 2/8, MANIT Colony, Bhopal, 462051, M.P., India

Abstract: The massive volumes of molecular data are available in online databases. The different types of inherent uncertainties present in this molecular data pose major challenge in the analysis of this data. The existing algorithms for association rule mining are not completely capable in dealing with these uncertainties. In the present paper a fuzzy soft fuzzy approach is proposed for mining amino acid associations in molecular sequences of Mycobacterium tuberculosis complex (MTBC). The data consisting of peptide sequences of MTBC is transformed into fuzzy transactional dataset using the fuzzy set. The soft set is employed to transform fuzzy transactions into soft fuzzy transactions. The fuzzy set is again employed to transform soft fuzzy transaction into fuzzy soft fuzzy transactions. The association rule mining is performed to generate amino acid association patterns. It is observed that the proposed approach prunes the spurious patterns and recovers the missing patterns obtained by the existing approaches.

Keywords: data mining; association rule; support; confidence; fuzzy set; soft set; transaction; fuzzy-soft-fuzzy set; Mycobacterium tuberculosis complex; uncertainty.

DOI: 10.1504/IJDMB.2017.084021

International Journal of Data Mining and Bioinformatics, 2017 Vol.17 No.1, pp.1 - 24

Received: 14 Nov 2015
Accepted: 24 Nov 2016
Published online: 03 May 2017 *

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