Treatment based on the syndrome differentiation is the key of traditional Chinese medicine (TCM) treating acquired immune deficiency syndrome (AIDS). Syndrome differentiation, where the patients suffering from a western medicine disease are divided into several classes based on their symptoms and signs, is an important diagnostic method and affects the effective use of TCM treatments. Some researches show that the clustering algorithms make it possible to classify the AIDS patients into several syndrome types. These algorithms improve the precision of syndrome differentiation so as to promote the TCM treatment efficacy. However, because of the complexity of AIDS disease, the AIDS clinical data usually have a large number of dimensions. The previous cluster algorithms assign equal weights to these dimensions and become confounded in the process of dealing with these dimensions. In this paper, we use a top-down subspace clustering algorithm as a solution to the syndrome differentiation. For a given cluster, we determine the relevant symptoms based on histogram analysis and assign greater weight to the relevant symptoms as compared to less relevant symptoms. Then, the symptoms with greater weight are used to differentiate the syndrome type of AIDS patients. Finally, the proposed method is compared with the traditional k-means algorithm based on the collected AIDS dataset. We evaluate their performance by the precision and the consistency. The experimental results show that the proposed algorithm is better than the traditional ones for aided TCM syndrome differentiation of AIDS patients.