Improve Semi-Supervised Fuzzy C-means Clustering Based On Feature Weighting
نویسندگان
چکیده
Semi-supervised learning is somewhere between unsupervised and supervised learning. In fact, most semi-supervised learning strategies are based on extending either unsupervised or supervised learning to include additional information typical of the other learning paradigm. Constraint fuzzy c-means a novel semi-supervised fuzzy c-means algorithm proposed by Li et al [1]. Constraint FCM like FCM ignores the characteristics of features. This affects the authenticity and accuracy of algorithm. This paper, proposes an improved Constraint FCM algorithm based on appropriate assigning weight to features. The experimental results on some UCI databases make it obvious that the proposed algorithm exhibits the better performance over Constraint FCM.
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