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
Semi-supervised fuzzy c-means clustering of biological data
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Adjusting Fuzzy Similarity Functions for use with standard data mining tools
Journal of Systems and Software
An algorithm for finding gene signatures supervised by survival time data
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Semi-supervised clustering with discriminative random fields
Pattern Recognition
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Semi Supervised methods use a small amount of auxiliary information as a guide in the learning process in presence of unlabeled data. When using a clustering algorithm, the auxiliary information has the form of Side Information, that is a list of co-clustered points. 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, as is the case of data deriving from biological experiments. 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 way to improve the classical fuzzy c-means clustering through a two steps procedure: first a series of metrics (one for each cluster) that satisfy a randomly generated set of constraints are learnt based on the data; then a generalized version of the fuzzy c-means (with the metrics computed in the previous step) is executed. We show the benefits and the limitations of this method using real world datasets and a modified version of the Partition Entropy index.