This paper proposes a novel Hierarchical Clustering algorithm based on the Micro-Cluster strategy, called HCMC algorithm. In order to alleviate the influence caused by noise on the quality of clustering, the concept of density-based is applied to data partitioning and filtration of noise ratio. HCMC algorithm consists in two phases clustering. The first phase aims to partition several micro-clusters and to filter noise ratio with the operation of density-based, which saves the main materials of clusters. The second phase proceeds to encapsulate, applying single-linkage agglomerative algorithm to explore of arbitrary shapes. With this phase of partitioning, the process of encapsulation develops efficiency. Lastly, this experiment demonstrates that HCMC algorithm is capable of reducing the impact on clustering caused by noise ratio and keeping a fair quality of clustering as well. In the meantime, HCMC algorithm also proves to be superior to other clustering algorithms among the same categories as far as the complicated time calculation is concerned.