Multi-Core Processing Cloud Eclat Growth
1V.Priya, Assistant Professor B.Sc Computer Science from University of Madras, M. Sc Computer Science from Nehru Memorial College, Puthanampatti, Bharathidasan University, M. Phil Computer Science from Bharathidasan University, Tamil Nadu.
2S.Murugan, Associate Professor in Nehru Memorial College M.Sc degree in Applied Mathematics from Anna University, M. Phil degree in Computer Science from National Institute of Technology formerly known as Regional Engineering College, Trichirappalli, Tamil Nadu.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 4063-4072 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8633088619/2019©BEIESP | DOI: 10.35940/ijeat.F8633.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Cloud parallel processing, Data Mining, Eclat Growth, Fixed point arithmetic, Graphics Processing Units, Hierarchical offloading, Multi-core processing.