Clustering is an important research topic in data mining domain for data preprocessing. Clustering is an unsupervised learning method that tries to find out some obvious clusters in the unlabeled data. It is usually performed by maximizing the similarity of inner-clusters and minimizing the similarity of inter-clusters. A lot of clustering algorithms have been proposed to solve various tasks and data properties in the past decades. However, all existing clustering methods have their own pros and cons, and there still lack of a clustering method with universality. Traditional clustering methods are usually classified into partitioning methods, hierarchical methods, density-based methods, grid-based methods and model-based methods. With a brief review to classical clustering methods, we put emphasis on introducing some recent emerging clustering methods like synchronization clustering algorithm, affinity propagation algorithm and density peaks algorithm. Based on the analysis and comparison of these algorithms, their potential applications and research directions are also discussed.