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Density-Based Clustering Method for Trends Analysis Using Evolving Data Stream

Density-Based Clustering Method for Trends Analysis Using Evolving Data Stream
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Author(s): Umesh Kokate (SP Pune University, India), Arviand V. Deshpande (SKN College of Engineering, India) and Parikshit N. Mahalle (SKN College of Engineering, India)
Copyright: 2020
Volume: 11
Issue: 2
Pages: 18
Source title: International Journal of Synthetic Emotions (IJSE)
Editor(s)-in-Chief: Amira S. Ashour (Tanta University, Egypt) and Nilanjan Dey (JIS University, Kolkata, India)
DOI: 10.4018/IJSE.2020070102

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Abstract

Evolution of data in the data stream environment generates patterns at different time instances. The cluster formation changes with respect to time because of the behaviour and members of clusters. Data stream clustering (DSC) allows us to investigate the changes of the group behaviour. These changes in the behaviour of the group members over time lead to formation of new clusters and may make old clusters extinct. Also, these extinct old clusters may recur over time. The problem is to identify and record these change patterns of evolving data streams. The knowledge obtained from these change patterns is then used for trends analysis over evolving data streams. In order to address this flexible clustering requirement, density-based clustering method is proposed to dynamically cluster evolving data streams. The decay factor identifies formation of new clusters and diminishing of older clusters on arrival of data points. This indicates trends in evolving data streams.

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