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A Kind of Data Stream Clustering Algorithm Based on Grid and Relative Density

Hongbin Gao, Ruiguang Li, Jie Hou

Open publisher page 3 citations

Abstract

This paper provided a Data Stream Clustering Algorithm Based on Grid and Relative Density which inherits the advantages of grid based clustering and relative density based clustering which can discover arbitrary-shape and multi-resolution clusters. Meanwhile introduced a concept of support which made algorithm adopt thoughts based on distance-based algorithm, provide the way to solve the effects of data object around grid on grid density.

About this research paper

What this paper is about

This paper provided a Data Stream Clustering Algorithm Based on Grid and Relative Density which inherits the advantages of grid based clustering and relative density based clustering which can discover arbitrary-shape and multi-resolution clusters. Meanwhile introduced a concept of support which made algorithm adopt thoughts based on distance-based algorithm, provide the way to solve the effects of data object around grid on grid density.

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OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Available abstract

This paper provided a Data Stream Clustering Algorithm Based on Grid and Relative Density which inherits the advantages of grid based clustering and relative density based clustering which can discover arbitrary-shape and multi-resolution clusters. Meanwhile introduced a concept of support which made algorithm adopt thoughts based on distance-based algorithm, provide the way to solve the effects of data object around grid on grid density.

Key concepts: Cluster analysis, Computer science, Data stream clustering, Grid, CURE data clustering algorithm, Data mining, Algorithm, Canopy clustering algorithm

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