MULTI-GRANULATION SPARK-BASED SUPER-TRUST FUZZY METHOD FOR LARGE-SCALE BRAIN MEDICAL RECORD SEGMENTATION

摘要

A multi-granulation Spark-based super-trust fuzzy method for large-scale brain medical record segmentation, comprising: first, segmenting a large-scale brain medical record data attribute set into different multi-granulation evolutionary subpopulations (Granu-populationi) on a Spark cloud platform; designing a multi-granulation Spark-based super-trust model to construct trust between different super elitists in multi-granulation populations; adjusting a multi-granulation center threshold, and dynamically updating the super elitists using a multi-granulation subpopulation balance adjustment strategy, performing global search segmentation and local refinement segmentation on large-scale brain medical records, wherein super elitists can collaboratively extract knowledge reduction subsets in respective regions; and finally, obtaining the optimal large-scale brain medical record segmentation characteristic set and storing same on the Spark cloud platform. By means of the present method, stable segmentation can be implemented on large-scale brain medical record knowledge reduction sets to provide important diagnostic basis for intelligent diagnosis and auxiliary treatment of brain diseases.