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Concept Drift Detection in Data Stream Clustering and its Application on Weather Data

Concept Drift Detection in Data Stream Clustering and its Application on Weather Data
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Author(s): Namitha K. (Artificial Intelligence and Computer Vision Lab, Department of Computer Science, Cochin University of Science and Technology, Kochi, Kerala, India)and Santhosh Kumar G. (Artificial Intelligence and Computer Vision Lab, Department of Computer Science, Cochin University of Science and Technology, Kochi, Kerala, India)
Copyright: 2020
Volume: 11
Issue: 1
Pages: 19
Source title: International Journal of Agricultural and Environmental Information Systems (IJAEIS)
Editor(s)-in-Chief: Frederic Andres (National Institute of Informatics, Japan), Chutiporn Anutariya (Asian Institute of Technology, Thailand), Teeradaj Racharak (Japan Advanced Institute of Science and Technology, Japan)and Watanee Jearanaiwongkul (National institute of Informatics, Japan)
DOI: 10.4018/IJAEIS.2020010104

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Abstract

This article presents a stream mining framework to cluster the data stream and monitor its evolution. Even though concept drift is expected to be present in data streams, explicit drift detection is rarely done in stream clustering algorithms. The proposed framework is capable of explicit concept drift detection and cluster evolution analysis. Concept drift is caused by the changes in data distribution over time. Relationship between concept drift and the occurrence of physical events has been studied by applying the framework on the weather data stream. Experiments led to the conclusion that the concept drift accompanied by a change in the number of clusters indicates a significant weather event. This kind of online monitoring and its results can be utilized in weather forecasting systems in various ways. Weather data streams produced by automatic weather stations (AWS) are used to conduct this study.

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