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

Abstract

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.

A Progressive Sampling and RadeMacher Average for an Effective Frequent Pattern Mining in Big Data Environment

“…N Yamuna Devi [21] have introduced a parallel direct vertical map reduce (PDVMR) programming approach to mine the frequent item from UCI machine learning repository dataset. The suggested approach is comprised with two stages such as mapping and reduction.…”

An innovativefractal architecture model for implementing MapReduce in an open multiprocessing parallel environment

“…Due to the sheer amount of data, big data relies heavily on parallel computing technology to finish processing data quickly. Many specialized programming models and runtime systems have been created to support big data [4]. The map/reduce approach is utilized by Hadoop and Spark [5].…”