Improved Novel Clustering Technique for Diverse and Self-Motivated Traffic Data Stream for IoT Scenario
Akhila Nadimpalli1, R. Shiva Shankar2, Chalapathi Raju K3, A. Harish Varma4
1Akhila Nadimpalli, Department of Computer Science Engineering, SRKR Engineering College, Bhimavaram, India.
2R. Shiva Shankar, Department of Computer Science Engineering, SRKR Engineering College, Bhimavaram, India.
3Chalapathi Raju. K, Department of Electronics and Communication Engineering, SRKR Engineering College, Bhimavaram, India.
4A. Harish Varma, Department of Electronics and Communication Engineering, SRKR Engineering College, Bhimavaram, India.
Manuscript received on 28 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 2837-2841 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8703078919/19©BEIESP | DOI: 10.35940/ijitee.I8703.078919
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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: Technologies are changing day by day and IoT is worldwide data and may of great business important to various users. sTo create such reasonable data, majority adaptive and K-mediod clustering techniques are employed in data mining. In research work, it focus on comparing adaptive, K-medisod and novel clustering technique to internet-of-things data collection in ITSs (Intelligence Traffic System). In traffic DataStream is composed form online site, it challenges of 30,000 instances with 9 attributes, clusters formed after evaluation and number of clusters is identified after the evaluation. Proposed techniques are significant too easy than some other clustering techniques with respect to all computation recall and precision parameters. In traffic databases depends on the data separation and cluster enhancement that is quality of clusters. To resolve the major issues that over load the system or Centre’s in IoT which consequences the huge kind of data on internet. It evaluated a set of consequences experiments using token and manufacture data from traffic use case view where the traffic considerations from the city monitor. Comparison of clustering methods that helps in determining suitable clustering approach for the offer internet of things database which results in optimal performance metrics.
Index Terms: Clustering Approach, IoT, Intelligent Traffic Systems, Precision and Recall.
Scope of the Article: IoT