In many real-world clustering tasks, data objects are described by both quantitative and qualitative attributes. Attributes with semantically ordered qualitative values are very common and are usually coded according to their order (i.e., consecutive integers) for clustering. However, semantic order is not always globally interdependent with a certain clustering task. An intuitive case is that level of income (attribute) is not always positively correlated with the level of mental health (label). Using mismatched order surely forms a bottleneck to clustering performance, and conversely, the unsupervised clustering process prevents understanding of "true" order. Therefore, we proposed a novel learning paradigm to tune the value order. More specifically, we adjust the intra-attribute orders, and let this process learn mutually with object clustering, thus bridging the gap between value order and clustering task. To the best of our knowledge, this is the first attempt to learn ordinal relationships among qualitative attribute values. Extensive experiments with significance tests show that our method outperforms the existing relevant clustering approaches on qualitative attribute data.