Study on the mining method for specific fault data of multimedia sensor networks in cloud computing environment

When traditional methods are used to mine specific fault data of multimedia sensor networks in cloud computing environment, the related specific fault features are just simply extracted from the database, and the dynamic changes of specific data fault characteristics in the database are ignored, also, the mining performance of specific fault data is reduced, there exist many other limitations. A mining method for specific fault data of multimedia sensor networks in cloud computing environment based on decision tree method is proposed, after pretreatments like interference filtration, data integration and data reduction are applied to multimedia data, the decision tree method is introduced to generate fault discrimination tree, according to the characteristics of specific fault data to acquire refined discrimination rules, and complete fault classification, so as to realize the mining for specific fault data. The experimental results show that the proposed method has very high accuracy and efficiency of the mining, and requires low energy consumption, also has strong adaptability.