Abstract：In view of the existing double-indices fuzzy subspace clustering algorithm based on feature weighted distance(DI-FSC)doesn′t make full use of the known information of the datasets,put forward a new kind of clustering method using a small amount of supervision information to assist the process of clustering.By introducing the feature weighted distance to improve the disadvantage of using the Euclidean metric to compute the distance between data points.At the same time,adding the index andto the constraint conditions to increase the flexibility of the algorithm.Experimental results show that the proposed algorithm under little supervision information on real datasets has better clustering effect.
2017, Vol. 38 
