A new approach for semi-supervised clustering based on Fuzzy C-Means
Valmir Macario, Francisco de A.T. de Carvalho · 2010
In traditional machine learning applications, only labeled data is used to train the classifier. Labeled data are difficult, expensive, time-consuming and require human experts to be obtained in several real applications. Semi-supervised learning address this issue. Semi-supervised learning uses large amount of unlabeled data, combined with the labeled data, to build better classifiers. The semi-supervised algorithm could be an extension of an unsupervised algorithm. Such algorithm would be based on unsupervised clustering algorithms, adding a term in its objective function that makes use of labeled information to guide the learning process. This study presents a new algorithm for semi-supervised clustering based on Fuzzy C-Means algorithm. The classifier was evaluated and compared against two semi-supervised clustering algorithms in the context of learning from partially labeled data. The behavior of the proposed algorithm is discussed and the results are validated using cross-validation and the confidence interval. Thus, it was possible to certify the better accuracy performance of the new algorithm when a few labeled data are available.