Radviz is a radial visualization technique that maps data from multi-dimensional space onto a planar picture. The dimensions placed on the circumference of a circle, called dimension anchors, can be reordered to reveal different patterns in the dataset. Extending the number of dimensions can enhance the flexibility in the placement of dimension anchors to explore meaningful visualizations. This paper describes a method that rationally extends a dimension to multiple new dimensions in Radviz. This method first calculates the probability distribution histogram of a dimension. The mean shift algorithm is applied to get centers of probability density to segment the histogram, and then the dimension can be extended according to the number of segments of the histogram. The paper also suggests using Dunn's index and accuracy rate to find the optimal placement of DAs, so the better effect of visual clustering can be achieved and evaluated after the dimension expansion in Radviz. Finally, it demonstrates the effectiveness of the new approach on synthetic and real world datasets.