A stratified sampling based clustering algorithm for large-scale data

Large-scale data analysis is a challenging and relevant task for present-day research and industry. As a promising data analysis tool, clustering is becoming more important in the era of big data. In large-scale data clustering, sampling is an efficient and most widely used approximation technique. Recently, several sampling-based clustering algorithms have attracted considerable attention in large-scale data analysis owing to their efficiency. However, some of these existing algorithms have low clustering accuracy, whereas others have high computational complexity. To overcome these deficiencies, a stratified sampling based clustering algorithm for large-scale data is proposed in this paper. Its basic steps include: (1) obtaining a number of representative samples from different strata with a stratified sampling scheme, which are formed by locality sensitive hashing technique, (2) partitioning the chosen samples into different clusters using the fuzzy c-means clustering algorithm, (3) assigning the out-of-sample objects into their closest clusters via data labeling technique. The performance of the proposed algorithm is compared with the state-of-the-art sampling-based fuzzy c-means clustering algorithms on several large-scale data sets including synthetic and real ones. The experimental results show that the proposed algorithm outperforms the related algorithms in terms of clustering quality and computational efficiency for large-scale data sets.