Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Locally linear metric adaptation for semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Improving fuzzy clustering of biological data by metric learning with side information
International Journal of Approximate Reasoning
Fuzzy-Adaptive-Subspace-Iteration-Based Two-Way Clustering of Microarray Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Semi-supervised methods use a small amount of labeled data as a guide to unsupervised techniques. Recent literature shows better performance of these methods with respect to totally unsupervised ones even with a small amount of side-information This fact suggests that the use of semi-supervised methods may be useful especially in very difficult and noisy tasks where little a priori information is available. This is the case of biological datasets' classification. The two more frequently used paradigms to include side-information into clustering are Constrained Clustering and Metric Learning. In this paper we use a Metric Learning approach as a preliminary step to fuzzy clustering and we show that Semi-Supervised Fuzzy Clustering (SSFC) can be an effective tool for classification of biological datasets. We used three real biological datasets and a generalized version of the Partition Entropy index to validate our results. In all cases tested the metric learning step produced a better highlight of the datasets' clustering structure.