Semi-supervised locality-weight fuzzy c-means clustering based on seeds and one novel decision rule
Lei Gu, Xianling Lu · 2012
Because the semi-supervised clustering can take advantage of some labeled data also called seeds to affect the clustering of unlabeled data, this paper proposed a semi-supervised clustering method based on a locality-weight fuzzy c-means clustering algorithm. The presented clustering method uses some seeds for the initialization and applies one novel decision rule to reassigning the class label to one data. To investigate the effectiveness of our approach, several experiments are done on one artificial dataset and three real datasets. Experimental results show that our proposed method can improve the clustering performance significantly compared to some unsupervised and semi-supervised clustering algorithms.