An Adaptive Slide Window Security Method for Transaction Updation in Data Stream Mining
Jayendra Kumar1, Anitha Raju2
1Jayendra Kumar, Research Scholar, CSE, Koneru lakshmaiah Education Foundation, Vaddeswaram, Guntur Dist,.A.P. India.
2Dr. Anitha Raju, Associate Professor, CSE, Koneru lakshmaiah Education Foundation, Vaddeswaram, Guntur Dist.,A.P. India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2864-2869 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3573129219/2019©BEIESP | DOI: 10.35940/ijeat.B3573.129219
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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 steam mining has gained large interest in current research domain. Where various information’s are retrieved based on the content of the context, the accuracy of the input stream with respect to its privacy is a major challenge. Windowing technique is used an effective approach in providing security measure in data stream mining. The recent develop windowing approach operates using sliding window, where anonymity is focused by different processing rules. The linear search sliding window has a constraint of search overhead and loss of generality under distributed information. In this paper, a new adaptive window approach for privacy coding in data stream mining is proposed. This presented approach is developed with the concern of minimize the search overhead and accuracy in search mining performance using adaptive window monitoring.
Keywords: Slide window approach, adaptive window coding, data stream mining.