A Novel Hybrid Machine Learning based Frequent Item Extraction for Transactional Database

  • Divvela Srinivasa Rao, Dr.V.Sucharita


-In big data, the frequent itemset classification is an important framework for all applications.
Several techniques were used to mine the frequent items but for the collapsed and complex data, it is
difficult. So that, the current research work aimed to model a novel Frequent Pattern Growth- Hybrid Ant
Colony and African Buffalo Model (FPG-HACABM) is developed to overcome this issue and to reduce
the execution time. Moreover, the Fitness function of HACABM is utilized to calculate the support count
of each item and to improve the classification accuracy. Thus the proposed models classify the frequently
utilized item accurately and arranged that items in descending order. This helps to run the big data
transactional application effectively without any delay. Finally, the key metrics are validated with existing
models and attained better results by achieving a high accuracy rate as 99.82% and less execution time