An Efficient Fuzzy C-Means Method with Variable FV-TC for Data Sensitivity Calculation in a Cloud Computing Environment
Ashutosh Kumar Dubey
Ashutosh Kumar Dubey, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 4486-4490| Volume-9 Issue-1, October 2019 | Retrieval Number: A1760109119/2019©BEIESP | DOI: 10.35940/ijeat.A1760.109119
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: In this paper an efficient fuzzy c-means (FCM) method has been used for the data sensitivity estimation. For the saturation point estimation variable fuzziness value (FV)-termination criteria (TC) have been used in the cloud computing environment. Data preprocessing has been performed along with the five attributes. Three attributes are based on the cloud user input parameters and the remaining two are the automated attributes which are calculated automatically. Then FCM has been applied. The total clusters calculated by our method are three. Then weighted product model has been applied for the cluster sensitivity calculation based on the three clusters. Our mechanism has the capability to identify the need of high, medium and low-level securities.
Keywords: FCM, FV, TC, Data sensitivity, Cloud computing.