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Data Mining Techniques using Time Series Research
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1020.0982s1119
Subject(s) - data mining , computer science , data pre processing , knowledge extraction , field (mathematics) , data stream mining , series (stratigraphy) , preprocessor , time series , measure (data warehouse) , data science , similarity (geometry) , machine learning , artificial intelligence , mathematics , paleontology , biology , pure mathematics , image (mathematics)
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.

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