Dynamic Perception Rule Acquirement for Incomplete Data

Modern science is increasingly data-driven and collaborative in nature. Comparing to ordinary data processing, big data processing that is mixed with great missing date must be processed rapidly. Considering this requirement this paper proposes a dynamic perception rule acquirement algorithm to implement fast and accurate information decision supporting model for incomplete data. It is inevitable that information contains incomplete data, and huge information being processing require fast algorithm to complete knowledge extraction. The method based on dynamic perception rule can achieve automatic analysis and intelligent cognition for the information decision supporting. Based on direction of maximum entropy at any moment, the perception rule can improve the recognition rate. Furthermore the dynamic perception rule adopts the tolerant relation to accommodate the incomplete data processing capability. The simulative analysis of diesel engine fault shows that the dynamic perception rule can achieve fast information decision supporting and the accuracy is certainly improved even for incomplete data.