Abstract: With the rapid development of the Internet,new types of information dissemination methods are emerging.It leads to an explosion of data at an unprecedented rate.How to process and analyze huge raw data and turn it into usable knowledge for learning and utilization,has become an important topic of common concern for scientists and technical experts at home and abroad.The Bayesian approach provides rich hierarchical models,uncertainty quantification and prior specification,so in large-scale data background it is very attractive.The limited-iteration bisecting K-means algorithm preserves the clustering quality of the approximate standard bisecting K-means algorithm with higher computational efficiency,and it is more suitable for large data sets requiring faster processing speeds.Aiming at the low execution efficiency problem of the original coresets construction algorithm,the limited-iteration bisecting K-means algorithm is improved,making the clustering result obtained at a faster speed and the weight of the relevant data points calculated under the condition of ensuring the clustering effect,thus constructing the coresets.Experiments show that compared with the original algorithm,the improved algorithm has higher computational efficiency,similar approximation performance and better approximation effect in some cases.

Key words: Bayesian logistic regression, Coresets, Large-scale dataset, Limited-iteration bisecting K-means