Mixed data clustering based on a number of similar features

Finding the degree of similarity measurement is one of the challenges of mixed data clustering. In this article, it has been tried to design a more efficient method by innovating in three important parts of clustering. In the part of the general method, for assigning data objects to the cluster, in addition to the distance, attention is paid to the "number of similar features". Compared to assigning each object to a cluster, in cases where the distances are equal or close, the cluster center with the highest number of features similar to the given objects will be appropriate. This method is more accurate than the Hamming distance. To determine the cluster centers, instead of random selection, a more suitable object is identified with a distance-based method. In accuracy in three datasets, the proposed algorithm has performed at least two percent better than the other algorithms.