Multi-core processing cloud eclat growth

Abstract

Data mining is a lively process used in many leading technologies of this information era. Eclat growth is one of the best performance data mining algorithms. This work is indented to create a suave interface for Eclat growth algorithm to run in multi-core processor-based cloud computing environments. Recent improvements in processor manufacturing technology make it possible to create multi-core high performance Central Processing Units (CPUs) and Graphics Processing Units (GPUs). Many cloud services are already providing accessibility to these high-power processor virtual machines. The process of blending these technologies with Eclat Growth is proposed here in the name of “Multi-core Processing Cloud Eclat Growth” (MPCEG) to achieve higher processing speeds without compromising the standard data mining metrics such as Accuracy, Precision, Recall and F1-Score. New procedures for Cloud Parallel Processing, GPU Utilization, Annihilation of floating point arithmetic errors by fixed point replacement in GPUs and Hierarchical offloading aggregation are introduced in the construction process of proposed MPCEG.