The difficult problem of identifying dominant structures in unknown data sets has been elegantly addressed recently by a non-parametric information theoretic approach, the "Jump" method. The method employs an appropriate but fixed power transformation on the distortion-rate, D(R), curve estimated by the popular K-means algorithm. Although this approach yields good results asymptotically for higher dimensional spaces, in many practical cases involving lower dimensional spaces, a transformation function with a fixed power may not find the correct model order. The work presented here develops an objective function to derive a more suitable transformation function that minimizes classification error in low dimensional data sets. In addition, a number of carefully chosen K-means seeding methods based upon proper heuristic choices have been used to enhance the detection sensitivity and to allow a more accurate estimation. The proposed method has been evaluated for a large variety of datasets and compared with the original Jump method and other well-known order estimation methods such as Minimum Description Length (MDL), Akaike Information Criteria (AIC), and Consistent Akaike Information Criteria (CAIC), demonstrating superior overall performance. Comparative results for the Wisconsin Diagnostic Breast Cancer Dataset have been included. This modified information theoretic approach to model order estimation is expected to improve and validate diagnostic classification and detection of pre-cancerous lesions. Other applications such as finding plausible number of segments in image segmentation scenarios are also possible.