Decision Tree: A Predictive Modeling Tool Used in Cloud Trust Prediction
Archana B Saxena1, Meenu Dave2
1Archana B Saxena*, Associate Professor, JIMS Rohini, Delhi, India.
2Dr. Meenu Dave, Professor, Jagannath University, Jaipur, India.
Manuscript received on July 16, 2019. | Revised Manuscript received on August 25, 2019. | Manuscript published on August 30, 2019. | PP: 4779-4783 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8763088619/2019©BEIESP | DOI: 10.35940/ijeat.F8763.088619
Open Access | Ethics and Policies | Cite | Mendeley
© 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: Trust is one of the important challenges faced by the cloud industry. Ever increasing data theft cases are contributing in worsening this issue. Regarding trust, author has a perception that this challenge can be handled to some extend if consumer can evaluate “Trust Value “ of the provider or can predict the same on some reliable basis. Current research is using predictive modeling for predicting trustworthiness of cloud provider. This paper is an attempt to utilize the data mining algorithm for predictive modeling. Decision Tree, a supervised data mining algorithm has been used in the current work for making predictions. Certification attainment criteria as prime basis for trust evaluation. In current scenario, data mining algorithm will classify providers in category of low, medium and high category of trust on the basis of information displayed on the public domain.
Keywords: Cloud, Trust, Machine Learning, Predictive Modeling, Supervised, Decision Tree.