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The data points in the constraint set are divided into groups through inference; and each group is assigned to the feasible cluster which minimizes the sum of squared distances between all the points in the group and the corresponding centroid. Our theoretical analysis shows that the probability of points being assigned to the correct clusters is much higher by the new algorithm, compared to the conventional methods. 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