In many practical applications of data mining, instances for each cluster are often required to be balanced in number. However, the entropy-weighted K-means algorithm (EWKM) for independent subspace clustering leads to unbalanced partitioning and poor clustering quality. Therefore, this study defines a multi-objective entropy that takes balanced partitioning and feature distribution into account and then employs the entropy to improve the objective function of the EWKM algorithm. Furthermore, the study designs the solution process by using the iterative method and alternating direction method of multipliers and proposes the entropy-based balanced subspace K-means algorithm (EBSKM). Finally, the clustering experiments are conducted in public datasets such as UCI and UCR, and the results show that the proposed algorithm outperforms similar algorithms in terms of accuracy and balance.