Deep Fuzzy C-Means algorithm is applied to decide the hidden structure in the data set. It is commonly utilized when data limits are not characterized, and additional boundaries are expected to reduce the statistical closeness. In this paper, we propose a semi-supervised deep fuzzy C-Means algorithm that accommodates this elusiveness. It applies to a machine learning strategy that depends on algorithmic stream for dynamic data. With statistical data gave as a collection of numerical data set of two classes, in particular, named and unlabeled, the semi-supervised deep fuzzy c-means clustering gives a comparison and answer for a given data set. The clustering approach sees enrollment functions for fuzziness. The proposed structure for semi-supervised data set finds supervised data and isolates it from unsupervised data. Here, the expression “deep” characterizes the closeness in ℜ²space, which is utilized to improve precision along with the centers.