A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing maps
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A framework for ontology-driven subspace clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised clustering: probabilistic models, algorithms and experiments
Semi-supervised clustering: probabilistic models, algorithms and experiments
Knowledge guided analysis of microarray data
Journal of Biomedical Informatics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Journal of Biomedical Informatics
RECM: Relational evidential c-means algorithm
Pattern Recognition Letters
A novel semi-supervised fuzzy C-means clustering method
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Fuzzy clustering with viewpoints
IEEE Transactions on Fuzzy Systems
Clustering Indian stock market data for portfolio management
Expert Systems with Applications: An International Journal
A new geometric shape-based genetic clustering algorithm for the multi-depot vehicle routing problem
Expert Systems with Applications: An International Journal
Classification of surgical processes using dynamic time warping
Journal of Biomedical Informatics
Semi-supervised fuzzy clustering with metric learning and entropy regularization
Knowledge-Based Systems
Clustering genome data based on approximate matching
International Journal of Data Analysis Techniques and Strategies
Computers in Biology and Medicine
Fuzzy clustering with biological knowledge for gene selection
Applied Soft Computing
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We propose a novel semi-supervised clustering method called GO Fuzzy c-means, which enables the simultaneous use of biological knowledge and gene expression data in a probabilistic clustering algorithm. Our method is based on the fuzzy c-means clustering algorithm and utilizes the Gene Ontology annotations as prior knowledge to guide the process of grouping functionally related genes. Unlike traditional clustering methods, our method is capable of assigning genes to multiple clusters, which is a more appropriate representation of the behavior of genes. Two datasets of yeast (Saccharomyces cerevisiae) expression profiles were applied to compare our method with other state-of-the-art clustering methods. Our experiments show that our method can produce far better biologically meaningful clusters even with the use of a small percentage of Gene Ontology annotations. In addition, our experiments further indicate that the utilization of prior knowledge in our method can predict gene functions effectively. The source code is freely available at http://sysbio.fulton.asu.edu/gofuzzy/.