A Parallel Direct‐Vertical Map Reduce Programming model for an effective frequent pattern mining in a dispersed environment

• Published in: Concurrency and computation Vol. 33; no. 24
• Main Author: Yamuna Devi, N
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 25.12.2021; Wiley Subscription Services, Inc
• Subjects: Data analysis; Data mining; Dispersion; extraction; loading; Machine learning; Mapping; mining system; parallel frequent pattern mining; Pattern analysis; software architectures
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.6470

Summary Data mining methods face various problems when extracting the data analysis process. These problems are solved based on the frequent pattern mining (FPM). In the data mining area, the frequent pattern (FP) extraction is a dominant embassy while processing the databases. Despite the fact that it has proven its importance in various mining provinces, the consistent research work of researchers has provided numerous efforts in this environment through substantial innovations. Based on the expected challenges of pattern mining, the time and memory consumption while mining the hidden FP increases over time by the conventional FPM approaches. This article proposes a Parallel Direct‐Vertical Map Reduce Programming model to effectively extract the FPM in a dispersed environment and analyzed using the Sales transaction dataset as UCI Machine Learning Repository. The proposed model has two phases of working, namely, (i) mapping and (ii) reduction. Here, the proposed Parallel Direct‐Vertical method is utilized while mapping. The parallelism of the proposed model is implemented and executed based on the number of clusters of databases in architecture‐based software and its final outcome shows that the proposed model is more efficient than the existing methods.