Compared to the traditional K-Means, the MWK-Means needs to obtain feature weights adaptively and select suitable exponent. Choosing the Minkowski exponent in an unsupervised setting is a way to calculate three-scale values of each exponent. It gets the best of each exponent based on the selection criteria of three scales, and then gets the mean of two closer exponents. According to this strategy, we put forward a new strategy of selecting the Minkowski exponent based on ranking, ranked the values of three scales each. Then, we added the two closer rankings of each exponent as comprehensive rank and used them to determine the final exponent. This paper used the above two strategies of selecting Minkowski exponent to test UCI dataset. The result shows that the new strategy is better.