IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

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
View Sample PDF
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: Petraq Papajorgji (Canadian Institute of Technology, Tirana, Albania) and François Pinet (Irstea/Cemagref - Clermont Ferrand, France)
DOI: 10.4018/IJAEIS.2020010104

Purchase

View Concept Drift Detection in Data Stream Clustering and its Application on Weather Data on the publisher's website for pricing and purchasing information.

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.

Related Content

Marcos Roberto dos Santos, Guilherme Afonso Madalozzo, José Maurício Cunha Fernandes, Rafael Rieder. © 2020. 22 pages.
Badr-Eddine Boudriki Semlali, Chaker El Amrani, Guadalupe Ortiz. © 2020. 25 pages.
Maya Gopal P.S., Bhargavi Renta Chintala. © 2020. 19 pages.
Namitha K., Santhosh Kumar G.. © 2020. 19 pages.
Xiangyan Meng, Muyan Liu, Qiufeng Wu. © 2020. 10 pages.
Lifang Fu, Xingchen Lv, Qiufeng Wu, Chengyan Pei. © 2020. 13 pages.
Dalma Radványi, András Geösel, Zsuzsa Jókai, Péter Fodor, Attila Gere. © 2020. 15 pages.
Body Bottom