An effective selecting approach for social media big data analysis — Taking commercial hotspot exploration with Weibo check-in data as an example

Abstract

According to the problem that efficient datasets cannot be quickly obtained from social media big data of social networks in the process of focused mining and analysis. An effective selection method for clustering mining with spacetime large data is proposed. The effective selection method of clustering mining divides the spatiotemporal large data from the dimension of space, time or attribute. Then do exploratory spatial data analysis(ESDA) to the obtained subsets to get the datasets with the potential of clustering mining quickly. the proposed method is verified by using the Weibo check-in data in Wuhan which is between 2011 and 2015 to mine commercial hotspots. The experimental results show that the method can quickly and effectively excavate datasets from Weibo check-in data that can reflect the distribution of Wuhan business circle, and the excavate d datasets have the characteristics of high clustering, small volume, high precision. The effective selection method of clustering mining for spatiotemporal data can provide fast and effective methods and ideas for the process of crowd sourcing geographic data today.