Hierarchical clustering is an important research branch of cluster analysis that has extensive ranges of practical applications. Meanwhile, it still faces problems such as inaccurate, time-consuming, and difficulty in choosing linkage method. In this paper, we present a new Hierarchical Clustering method based on Local Cores and Sharing concept (HCLCS) which takes a "divide-and-merge" framework by first dividing a data set into several small clusters and then merging them hierarchically. To improve the accuracy, the merging process is further divided into two substeps: (1) pre-connect small clusters that belong very likely to the same category, and (2) merge the pre-connected intermediate clusters and the remaining unconnected small clusters in a classical hierarchical way. Extensive experiments on synthetic and real-world data sets show that HCLCS can achieve better performance than existing methods in dealing with data sets with complex structures and is less time-consuming than two state-of-the-art algorithms (SNN-DPC and RSC).