Cloud Model: Detect Unsupervised Communities in Social Tagging Networks

Abstract

In the big data era, detecting unsupervised communities in a given dataset, analyzing the evolution of the unsupervised communities, tracing the interests of users are very important. For instance, we can capture user's interest and provide personalized information. In order to detect unsupervised communities in social tagging networks, this paper uses similarity cloud properties of cloud model to solve the different community analysis, classification, and describe the evolutions of unsupervised communities quantitatively and users' dynamic interests in unsupervised communities problems. Cloud model is used in this paper. By introducing similarity cloud properties of cloud model, cloud model can detect the unsupervised communities, describe the evolutions of unsupervised communities quantitatively, and users' dynamic interests in unsupervised communities. For illustration, the proposed model is applied to Delicious dataset to detect unsupervised communities and one month is used as time slice to study the evolutions of the unsupervised communities. Empirical results show that the unsupervised community in social tagging in network, using Similarity cloud properties of cloud model can effectively detect different unsupervised communities, and describe the evolutions of unsupervised communities quantitatively. Similarity cloud properties based cloud model can effectively detect unsupervised community in social tagging network, and quantitatively describe the evolutions of the community and community user' dynamic interest. Hence, CBUCD model is an efficient solution for detecting unsupervised community and analyzing evolutions.