Semi-supervised subtractive clustering by seeding
Lei Gu, Xianling Lu · 2012
In this paper, a novel semi-supervised subtractive clustering algorithm by seeding is proposed. Like the semi-supervised clustering approaches based on K-Means, the presented method applies a small amount of labeled data called seeds to aid the traditional subtractive clustering. Experimental results show that the new method can improve the clustering performance significantly compared to other semi-supervised clustering algorithms.