Yang's coefficient for mixed and fuzzy feature variable was modified in terms of the aggregation technique and the entropy-based distance function. There are four proposed dissimilarity coefficients which used Yang's coefficient for numerical attributes and fuzzy attributes while entropy such as Havrda-Charvat's structural a-entropy and Jensen-Shannon divergence for categorical attributes. The hierarchical clustering method was used for clustering mixed and fuzzy data. Single linkage clustering algorithm and UPGMA were employed to generate fuzzy dendrograms. The type of data has a significant effect on the clustering produced accompanied by the aggregation function used and the entropy measure employed. The dataset used in the study were small and large dataset. For small data, the proposed dissimilarity coefficients that used De Carvalho's extension of Ichino and Yaguchi's dissimilarity measure as aggregation produced better clustering solution in small data. On the other hand, for large data, the proposed dissimilarity coefficients that used the two aggregation function such as De Carvalho's dissimilarity measure and De Carvalho's extension of Ichino and Yamguchi's dissimilarity measure worked well in clustering. The proposed dissimilarity coefficients were compared to the original Yang's coefficient measures for mixed feature variable and fuzzy data to evaluate the effectiveness and efficiency of the propose dissimilarity coefficients. The proposed dissimilarity coefficients performed better with mixed and fuzzy feature variable compared to the original Yang's dissimilarity measures mixed and fuzzy data.