An Improved Algorithm for Dynamic Cognitive Extraction Based on Fuzzy Rough Set

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. The Rough Set was generated to deal with the large data.In this paper, we proposed animproved algorithm for dynamic Cognitive extractionwhich deals with adaptive fuzzy attribute values and the fuzzy attribute reduction aiming at uncertainty datasuch asdata with diversity or missing character faced by the big data with using Fuzzy Rough Set Theory.At the aspect of information decision, according to the Real-time input information, it deep analyzes the dynamic information entropy of the data itself and chooses the biggest prediction information entropy direction for the cognitive rules to achieve rapid recognitive of data, complete information of quick decision.Because the algorithm is adopted to predict the best direction of information entropy, so the recognitive effect is also improved. At the end of the paper, we have analyzed superiority of the dynamic cognitive algorithm by using breast cancer data as the foundation.