Rough-Fuzzy C-Medoids Algorithm and Selection of Bio-Basis for Amino Acid Sequence Analysis

Abstract

Abstract-In most pattern recognition algorithms, amino acids cannot be used directly as inputs since they are nonnumerical variables. They, therefore, need encoding prior to input. In this regard, bio-basis function maps a nonnumerical sequence space to a numerical feature space. It is designed using an amino acid mutation matrix. One of the important issues for the bio-basis function is how to select the minimum set of bio-bases with maximum information. In this paper, we describe an algorithm, termed as rough-fuzzy c-medoids (RFCMdd) algorithm, to select the most informative bio-bases. It is comprised of a judicious integration of the principles of rough sets, fuzzy sets, the c-medoids algorithm, and the amino acid mutation matrix. While the membership function of fuzzy sets enables efficient handling of overlapping partitions, the concept of lower and upper bounds of rough sets deals with uncertainty, vagueness, and incompleteness in class definition. The concept of crisp lower bound and fuzzy boundary of a class, introduced in RFCMdd, enables efficient selection of the minimum set of the most informative bio-bases. Some new indices are introduced for evaluating quantitatively the quality of selected bio-bases. The effectiveness of the proposed algorithm, along with a comparison with other algorithms, has been demonstrated on different types of protein data sets.

AVLINK: Robust Clustering Algorithm based on Average Link Applied to Protein Sequence Analysis

“…The study most relevant to our focus here is [13] , [22], [28] and [29]. In [22] a fuzzy clustering for a relational data termed as FCMdd (Fuzzy C-Medoids) is proposed and compared with the Relational Fuzzy C-Means algorithm (RFCM) and found to be more efficient.…”

“…In [22] a fuzzy clustering for a relational data termed as FCMdd (Fuzzy C-Medoids) is proposed and compared with the Relational Fuzzy C-Means algorithm (RFCM) and found to be more efficient. In [13] the principles of rough sets, fuzzy sets [15] is applied to both the hard and fuzzy c-medoids algorithm [22] and rough-fuzzy c-medoids algorithm is proposed to select the most informative bio-bases [14]. The amino acid mutation matrix [16] is used in computing the similarity matrix of subsequences in [13].…”

“…In [13] the principles of rough sets, fuzzy sets [15] is applied to both the hard and fuzzy c-medoids algorithm [22] and rough-fuzzy c-medoids algorithm is proposed to select the most informative bio-bases [14]. The amino acid mutation matrix [16] is used in computing the similarity matrix of subsequences in [13]. In [28] authors uses randomized search along with soft clustering [23] to reduce the complexity of the rough fuzzy c-medoids algorithms [13].…”

“…The amino acid mutation matrix [16] is used in computing the similarity matrix of subsequences in [13]. In [28] authors uses randomized search along with soft clustering [23] to reduce the complexity of the rough fuzzy c-medoids algorithms [13].…”

“…An initialization procedure is proposed that does not require several input parameters as in [13] instead it needs only one parameter (a threshold on the average similarity within a cluster) that can be systematically estimated as described later. Also specifying a threshold on the average similarity within a cluster is easy for a user to understand.…”