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Cloud Optimized Eclat Growth (COEG) Using Fuzzy Logic

International Journal of Distributed and Cloud Computing

Volume 7 Issue 1

Published: 2019
Author(s) Name: Priya Vaithiyanathan and Murugan S. | Author(s) Affiliation: Assist. Prof., Dept. of Comp. Scie., Nehru Memorial College, Bharathidasan Univ., Tamil Nadu, India.
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Abstract

Eclat is one of the best data mining algorithms in practice. The basic Eclat algorithm has some limitations and an improved version of Eclat was introduced in the name of Eclat-Growth to overcome some of the limitations. Cloud Optimized Eclat Growth (COEG) method is proposed in this work in which a new Eclat based data mining approach is introduced that overcomes the disadvantages of existing methods to work well with real-time cloud environments. New concepts of localized cloud Eclat data processing by cloud offloading method introduced in this work. The localized cloud data processing procedure reduces the process of repeated global database scanning to improve processing speed and to reduce the memory consumption in a single machine. Fuzzy logic based multidimensional quantitative itemsets table is used to improve the accuracy. Since the proposed method uses localized cloud data processing, multidimensional liked lists are used which improves the item, transaction and pattern-based search capabilities. COEG is prepared with the intension to maximize the accuracy, precision, recall, intra-cluster density and to minimize the processing time and memory utilization.

Keywords: Accuracy, Cloud data processing, Eclat, Fuzzy logic, Optimization.

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