The aim of Semi-supervised clustering algorithm is to improve the clustering performance by considering the user supervision based on the pairwise constraints. In this paper, we examine the active learning challenges to choose the pairwise must-link and cannot-link constraints for semi-supervised clustering. The proposed active learning approach increases the neighborhoods based on selecting the in formative points and querying their relationship among the neighborhoods. Here, the classic uncertainty-based principle is designed and novel approach is presented for calculating the uncertainty associated with each data point. Further, a selection criterion is introduced that trades off the amount of uncertainty of each data point with the probable number of queries (the cost)essential to determine this uncertainty. This permits us to select queries that have the maximum information rate. The proposed method is evaluated on the benchmark data sets and the results shows that the proposed system yields better outputs over the current state of the art.