The Application of Improved FP-Growth Algorithm in Disease Complications

Abstract

With the rapid development of computer technology, data mining using association rules has been widely used in all walks of life. FP-Growth algorithm is an association rule algorithm. It does not need to generate candidates, so it has practical value for the study of disease complications. However, it is inefficient and prone to spillover due to its large scale medical data. This paper proposes an improved algorithm based on the FP-Growth algorithm, which sets the minimum support threshold and deletes infrequent item sets to improve the operation efficiency. Experimental results show that this algorithm accelerates the speed of data mining and improves the processing efficiency in the medical data environment.