The use of unlabeled data has leads to improvement in classification accuracy for a variety of classification problems via co-training approaches. In the co-training approach, the data has to be available in a dual view representation or two distinct classifiers are required. In this paper, an unified energy equation for classification combining labeled data and unlabeled data is introduced. This classification formulation is posed as a constrained minimum cut problem integrating labeling information on labeled data and cluster similarity information on unlabeled data for joint estimation. A novel constrained randomized contraction algorithm is proposed for finding the solution to the constrained minimum cuts problem. Experimental results on standard datasets and synthetic datasets are presented.