World Scientific
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at customercare@wspc.com for any enquiries.

A novel hybrid machine learning-based frequent item extraction for transactional database

    https://doi.org/10.1142/S1793962323410064Cited by:0 (Source: Crossref)

    In big data, the frequent item set mining is an important framework for many applications. Several techniques were used to mine the frequent item sets, but for the collapsed and complex data, it is difficult. Hence, 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 items accurately and arranged those items in descending order. This helps to run the big data transactional application effectively without any delay. Finally, the key metrics are validated with the existing models and better results are attained by achieving a high accuracy rate of 99.82% and less execution time of 0.0018 ms.

    Remember to check out the Most Cited Articles!

    Check out our handbook collection in computer science!