Clustering is a very useful machine learning technique to find the underlying classification of unlabeled data. In computational biology, clustering techniques are extensively used to identify a group of biomolecules responsible for biological activity in animals. There are several types of knowledge acquisition techniques are used in Clustering analysis. Most of them are work best for a particular data type. Now, it is very difficult for researchers to choose appropriate clustering technique for a specific dataset. Therefore, we present a comprehensive comparative analysis of broadly classified clustering techniques over the biological dataset. Here we consider 4 types of datasets for our experiment. As a result of this comparative analysis, we found that using the partition base algorithm on all the data, the K-means clustering algorithm is giving the better results and in the case of the non-partition base algorithm, the Hierarchical clustering algorithm using complete linkage method is giving the better results than others. This study will further help us to find more efficient clustering technique that works well with all type of biological $dataset$.