Data objects with mixed numeric and categorical attributes are commonly encountered in real world. The k-prototypes algorithm is one of the principals for clustering this type of data objects. An improved k-prototypes algorithm is proposed to cluster mixed data in this paper. In our method, the concept of the distribution centroid is introduced for representing the prototype of categorical attributes in a cluster. Then we combine both mean with distribution centroid to represent the prototype of the cluster with mixed attributes, and thus propose a new measure to calculate the dissimilarity between data objects and prototypes of clusters. This measure takes into account the significance of different attributes towards the clustering process. Finally, we present out algorithm for clustering mixed data, and the performance of our method is demonstrated by a series of experiments on three real-world datasets in comparison with that of traditional clustering algorithm.