Algorithms for clustering data
Algorithms for clustering data
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
Neural Networks and Genome Informatics
Neural Networks and Genome Informatics
New techniques for extracting features from protein sequences
IBM Systems Journal - Deep computing for the life sciences
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Design of a two-stage fuzzy classification model
Expert Systems with Applications: An International Journal
Application of fuzzy subtractive clustering for enzymes classification
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Engineering Applications of Artificial Intelligence
A new multi-objective technique for differential fuzzy clustering
Applied Soft Computing
Protein feature classification using particle swarm optimization and artificial neural networks
Proceedings of the 2011 International Conference on Communication, Computing & Security
Information Sciences: an International Journal
Improved differential evolution for microarray analysis
International Journal of Data Mining and Bioinformatics
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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In this article, we propose an efficient technique for classifying amino acid sequences into different superfamilies. The proposed method first extracts 20 features from a set of training sequences. The extracted features are such that they take into consideration the probabilities of occurrences of the amino acids in the different positions of the sequences. Thereafter, a genetic fuzzy clustering approach is used to automatically evolve a set of prototypes representing each class. The characteristic of this clustering method is that it does not require the a priori information about the number of clusters, and is also able to come out of locally optimal configurations. Finally, the nearest neighbor rule is used to classify an unknown sequence into a particular superfamily class, based on its proximity to the prototypes evolved using the genetic fuzzy clustering technique. This results in a significant improvement in the time required for classifying unknown sequences. Results for three superfamilies, namely globin, trypsin and ras, demonstrate the effectiveness of the proposed technique with respect to the case where all the training sequences are considered for classification using the same set of features. Comparison with the well-known technique BLAST also shows that the proposed method provides a significant improvement in terms of the time required for classification while providing comparable classification performance.