Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Data mining: concepts and techniques
Data mining: concepts and techniques
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Fuzzy Modeling for Control
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Neural Networks for Financial Forecasting
Neural Networks for Financial Forecasting
Bioinformatics Methods and Protocols
Bioinformatics Methods and Protocols
Clustering Large Graphs via the Singular Value Decomposition
Machine Learning
Protein Classification into Domains of Life Using Markov Chain Models
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
The uniqueness of a good optimum for K-means
ICML '06 Proceedings of the 23rd international conference on Machine learning
Comparative genome sequence analysis by efficient pattern matching technique
WSEAS Transactions on Information Science and Applications
Fuzzy c-means clustering with prior biological knowledge
Journal of Biomedical Informatics
Classification of DNA Sequences Basing on the Dinucleotide Compositions
ISCID '09 Proceedings of the 2009 Second International Symposium on Computational Intelligence and Design - Volume 02
Fuzzy pattern extraction for classification of protein sequences
ISB '10 Proceedings of the International Symposium on Biocomputing
Neural Computing and Applications
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
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Genome data mining and knowledge extraction is an important problem in bioinformatics. Some research work has been done for genome identification based on exact matching of n-grams. However, in most real world biological problems, it may not be feasible to have an exact match, so approximate matching may be desired. The problem in using n-grams is that the number of features 4n for DNA sequence and 20n for protein sequence increases with increase in n. In this paper, we propose an approach for genome data clustering based on approximate matching. Generally genome sequences are very long, so we sample the data into 10,000 base pairs. Given a database of genome sequences, our proposed work includes extraction of total number of approximate matching patterns to a query with given fault tolerance and then using this total number of matches for clustering. Candidate length is varied so as to allow both positive and negative tolerance and hence the number of features used for clustering also varies. K-means, fuzzy C-means FCM and possibilistic C-means PCM algorithms are used for clustering of the genome data. Experimental results obtained by varying tolerance from 20% to 70% are reported. It has been observed that as tolerance increases, number of genome samples that are correctly clustered also increases and our proposed approach outperforms existing n-gram frequency based approach. Two different genome datasets are used to verify the proposed method namely yeast, E. coli and Drosophila, mouse.