NebFuzz: A New Semi-Supervised Clustering Algorithm Based on Fuzzy C-Means

• Valmir Macário UFPE
• Francisco de A. T. de Carvalho UFPE

Abstract

Semi-supervised clustering uses unlabeled data, combined with the labeled data, to improve the algorithm performances. This paper presents a new algorithm for semi-supervised clustering based on Fuzzy C-Means algorithm. The new algorithm 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 accuracy rate, corrected rand index and a 95% confidence interval. Thus, it was possible to certify the better accuracy performance of the new algorithm when a few labeled data are available.

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