The Journal of Grey System ›› 2022, Vol. 34 ›› Issue (4): 90-.

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A Self-Adaptive Grey DBSCAN Clustering Method

  

  • Online:2022-12-25 Published:2022-12-29

Abstract:

Clustering analysis, as a classical issue in data mining, is widely applied in various research areas. This article proposes a self-adaptive grey DBSCAN (SAG-DBSCAN) clustering algorithm by introducing a grey relational matrix to obtain the grey local density indicator. We then apply this local indicator to have self-adaptive noise identification to gain a dense subset of the clustering data set. An advantage of this algorithm is that it can automatically estimate the parameters utilized to cluster the dense subset. Several frequently-used data sets are further examined to compare the performance and effectiveness of our proposed clustering algorithm with those of other state-of-the-art algorithms. The comparisons indicate that our new method outperforms other common methods. 

Key words: Clustering Analysis, Density-Based, B-Style Grey Relational Degree, SAG-DBSCAN