Abstract. This paper presents a hybrid relational clustering algorithm, termed as rough-fuzzy c-medoids, to cluster biological sequences. It com-prises a judicious integration of the principles of rough sets, fuzzy sets, c-medoids algorithm, and amino acid mutation matrix used in biology. The concept of crisp lower bound and fuzzy boundary of a class, in-troduced in rough-fuzzy c-medoids, enables ecient selection of cluster prototypes. The eectiveness of the algorithm, along with a comparison with other algorithms, is demonstrated on dierent protein data sets.
A hybrid unsupervised learning algorithm, termed as rough-fuzzy c-means, is proposed in this paper. ...
Clustering is the identification of interesting distribution patterns and similarities, natural grou...
AbstractEstablishing a classification model for cancer recognition based on DNA microarrays is usefu...
To recognize functional sites within a protein sequence, the non-numerical attributes of the sequenc...
In most pattern recognition algorithms, amino acids cannot be used directly as inputs since they are...
Biological data classification and analysis are significant for living organs. A biological data cla...
<div><p>In this paper, we present a novel rough-fuzzy clustering (RFC) method to detect overlapping ...
In this paper, we present a novel rough-fuzzy clustering (RFC) method to detect overlapping protein ...
Data clustering is a key task for various processes including sequence analysis and pattern recognit...
The article describes a research about fuzzy clustering algorithms, their creation and classificatio...
Cancer molecular pattern efficient discovery is essential in the molecular diagnostics. The number o...
Abstract—Soft computing is gradually opening up several possi-bilities in bioinformatics, especially...
The unsupervised clustering of biological sequences is an important task in the bioinformatics space...
Clustering of data is a well-researched topic in computer sciences. Many approaches have been design...
Current copiousness of genomic information stored in biological databases makes ultimately feasible ...
A hybrid unsupervised learning algorithm, termed as rough-fuzzy c-means, is proposed in this paper. ...
Clustering is the identification of interesting distribution patterns and similarities, natural grou...
AbstractEstablishing a classification model for cancer recognition based on DNA microarrays is usefu...
To recognize functional sites within a protein sequence, the non-numerical attributes of the sequenc...
In most pattern recognition algorithms, amino acids cannot be used directly as inputs since they are...
Biological data classification and analysis are significant for living organs. A biological data cla...
<div><p>In this paper, we present a novel rough-fuzzy clustering (RFC) method to detect overlapping ...
In this paper, we present a novel rough-fuzzy clustering (RFC) method to detect overlapping protein ...
Data clustering is a key task for various processes including sequence analysis and pattern recognit...
The article describes a research about fuzzy clustering algorithms, their creation and classificatio...
Cancer molecular pattern efficient discovery is essential in the molecular diagnostics. The number o...
Abstract—Soft computing is gradually opening up several possi-bilities in bioinformatics, especially...
The unsupervised clustering of biological sequences is an important task in the bioinformatics space...
Clustering of data is a well-researched topic in computer sciences. Many approaches have been design...
Current copiousness of genomic information stored in biological databases makes ultimately feasible ...
A hybrid unsupervised learning algorithm, termed as rough-fuzzy c-means, is proposed in this paper. ...
Clustering is the identification of interesting distribution patterns and similarities, natural grou...
AbstractEstablishing a classification model for cancer recognition based on DNA microarrays is usefu...