Traditional clustering algorithms are always viewed as unsupervised methods for data grouping to extract information of interest from unlabeled data

while semi-supervised clustering employs limited amount of labeled data to aid the unsupervised grouping of mass unlabeled data. Pedrycz provided a semi-supervised Fuzzy C-Means algorithm (sFCM) to incorporate supervised information of labeled data as an additive part of objective function in the Fuzzy C-Means algorithm (FCM).This paper proposes a novel algorithm called Degeneracy-Improved Semi-Supervised Fuzzy C-Means algorithm (dsFCM) to fundamentally overcome the critical disadvantages of Pedrycz’s sFCM algorithm

i.e.

degeneracy to the classical FCM algorithm and slow convergence

particularly when applied in actual data set in which the amount of labeled points is far fewer than that of unlabeled points. Experimental results on UCI benchmark data and IBSR brain MR image data demonstrate that dsFCM algorithm can outperform sFCM algorithm in accuracy

speed and robustness. Moreover

it shows that dsFCM algorithm avoids the problems of slow convergence and degeneracy to classical FCM algorithm when applied to real world data clustering with exiguous labeled data

and presents its effectiveness for the application in interactive segmentation of medical images with a small amount of labeled data points given by user.