A user credit assessment model based on clustering ensemble for broadband network new media service supervision

摘要

This paper proposes a user credit assessment model based on clustering ensemble aiming to solve the problem thatusers illegally spread pirated and pornographic media contents within the user self-service oriented broadband networknew media platforms. Its idea is to do the new media user credit assessment by establishing indices system based on usercredit behaviors, and the illegal users could be found according to the credit assessment results, thus to curb the badvideos and audios transmitted on the network.The user credit assessment model based on clustering ensemble proposed by this paper which integrates theadvantages that swarm intelligence clustering is suitable for user credit behavior analysis and K-means clustering couldeliminate the scattered users existed in the result of swarm intelligence clustering, thus to realize all the users' creditclassification automatically.The model's effective verification experiments are accomplished which are based on standard credit applicationdataset in UCI machine learning repository, and the statistical results of a comparative experiment with a single modelof swarm intelligence clustering indicates this clustering ensemble model has a stronger creditworthiness distinguishingability, especially in the aspect of predicting to find user clusters with the best credit and worst credit, which willfacilitate the operators to take incentive measures or punitive measures accurately. Besides, compared with theexperimental results of Logistic regression based model under the same conditions, this clustering ensemble model isrobustness and has better prediction accuracy.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.