Data Mining Techniques Using Time Series Research
D. Senthil1, G. Suseendran2
1D. Senthil, Ph.D, Research Scholar, Department of Computer Science, Vels Institute of Science, Technology & Advanced Studies VISTAS, Chennai (Tamil Nadu), India.
2Dr. G. Suseendran, Assistant Professor, Department of Information and Technology, Vels Institute of Science, Technology & Advanced Studies VISTAS, Chennai (Tamil Nadu), India.
Manuscript received on 10 October 2019 | Revised Manuscript received on 19 October 2019 | Manuscript Published on 02 November 2019 | PP: 121-129 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10200982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1020.0982S1119
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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: As time-series data are eventually large the discovery of knowledge from these massive data seems to be a challenge issue. The similarity measure plays a primary role in time series data mining, which improves the accuracy of data mining task. Time series data mining is used to mine all useful knowledge from the profile of data. Obviously, we have a potential to perform these works, but it leads to a vague crisis. This paper involves a survey regarding time series technique and its related issues like challenges, preprocessing methods, pattern mining and rule discovery using data mining. Streaming of data is one of the difficult tasks that should be managed over time. Thus, this paper can provide a basic and prominent knowledge about time series in data mining research field.
Keywords: Data Streaming, Preprocessing Methods and Time Series Analysis.
Scope of the Article: Data Mining