Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Evolutionary semi-supervised fuzzy clustering
Pattern Recognition Letters
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Analyzing Gene Expression Time-Courses
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Measuring semantic similarity between Gene Ontology terms
Data & Knowledge Engineering
Active semi-supervised fuzzy clustering
Pattern Recognition
An improved algorithm for clustering gene expression data
Bioinformatics
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Partially supervised clustering for image segmentation
Pattern Recognition
Clustering by competitive agglomeration
Pattern Recognition
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
Fuzzy clustering with partial supervision
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy intelligent approach to the classification problem in gene expression data analysis
Knowledge-Based Systems
Gene ontology semi-supervised possibilistic clustering of gene expression data
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
An efficient and scalable family of algorithms for combining clusterings
Engineering Applications of Artificial Intelligence
Semi-supervised clustering via multi-level random walk
Pattern Recognition
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Over the last decade there has been an increasing interest in semi-supervised clustering. Several studies have suggested that even a small amount of supervised information can significantly improve the results of unsupervised learning. One popular method of incorporating partial supervised information is through pair-wise constraints indicating whether a certain pair of patterns should belong to the same (Must-link) or different (Dont-link) clusters. In this study we propose a novel semi-supervised fuzzy clustering algorithm (SSFCA). The supervised information is incorporated via a method quantifying Must-link and/or Dont-link constraints. Additionally, we present an extension of SSFCA that allows the algorithm to automatically detect the number of clusters in the data. We apply SSFCA to the intrinsic problem of gene expression profiles clustering. The advantageous properties of fuzzy logic, inherited to SSFCA, allow genes to belong to more than one group, revealing this way more profound information concerning their multiple functioning roles. Finally, we investigate the incorporation of prior biological knowledge arriving from Gene Ontology in the process of selecting pair-wise constraints. Simulations on artificial and real life datasets proved that the proposed SSFCA significantly outperformed other standard and semi-supervised clustering methods.