In recent days, mining data in the form of information and knowledge from large databases is one of the demanding and task. Finding similarity between different attributes in a synthetic dataset is an aggressive concept in data retrieval applications. For this purpose, some of the clustering techniques are proposed in the existing works such as k-means, fuzzy c-means, and fuzzy k-means. But it has some drawbacks that include high overhead, less effective results, computation complexity, high time consumption, and memory utilization. To overcome these drawbacks, a similarity-based categorical data clustering technique is proposed. Here, the similarities of inter- and intra-attributes are simultaneously calculated and it is integrated to improve the performance. The dataset loaded as input, where the preprocessing is performed to remove the noise. Once the data are noise free, the similarity between the elements is computed; then, the most relevant attributes are selected and the insignificant attributes are neglected. The support and confidence measures are estimated by applying association rule mining for resource planning. The similarity-based K-medoids clustering technique is used to cluster the attributes based on the Euclidean distance to reduce the overhead. Finally, the bee colony (BC) optimization technique is used to select the optimal features for further use. In experiments, the results of the proposed clustering system are estimated and analyzed with respect to the clustering accuracy, execution time (s), error rate, convergence time (s), and adjusted Rand index (ARI). From the results, it is observed that the proposed technique provides better results when compared to the other techniques.