ANN-DPC: Density peak clustering by finding the adaptive nearest neighbors

DPC(clustering by fast search and find of density peaks) is an efficient clustering algorithm. However, DPC and its variations usually cannot detect the appropriate cluster centers for a dataset containing sparse and dense clusters simultaneously, resulting in the unique clustering within a dataset...

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Veröffentlicht in:Knowledge-based systems 2024-06, Vol.294, p.111748, Article 111748
Hauptverfasser: Yan, Huan, Wang, Mingzhao, Xie, Juanying
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Sprache:eng
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Zusammenfassung:DPC(clustering by fast search and find of density peaks) is an efficient clustering algorithm. However, DPC and its variations usually cannot detect the appropriate cluster centers for a dataset containing sparse and dense clusters simultaneously, resulting in the unique clustering within a dataset cannot being found. To remedy these limitations, we propose an Adaptive Nearest Neighbor Density Peak Clustering algorithm, referred to as ANN-DPC. It introduces the adaptive nearest neighbors for a point, so as to define the accurate local density of the point. Moreover, it partitions points into super-score, core, linked and slave points, and proposes techniques to detect appropriate cluster centers through introducing super-core point with higher local density to absorb the other super-core points sharing adaptive nearest neighbors with it and the dependency vector for finding next cluster center. Furthermore, novel assignment strategies are proposed by leveraging the adaptive nearest neighbors combing with breadth first search and fuzzy weighted adaptive nearest neighbors, so as to assign non-center points to the most appropriate clusters. Extensive experiments on real-world and synthetic datasets demonstrate the superiority of the proposed ANN-DPC algorithm over the counterparts in precisely detecting the cluster centers and the unique clustering within a dataset. [Display omitted] •Introducing adaptive nearest neighbors for a point to define its accurate local density and partition it as a super-core, core, linked, or slave point.•Introducing super-core point absorbing technique and the dependency vector to detect the appropriate cluster centers within a dataset.•New assignment strategies are present by leveraging the adaptive nearest neighbors combining with breadth first search and fuzzy weighted adaptive nearest neighbors.•ANN-DPC algorithm is introduced by leveraging these aforementioned contributions.•Extensive experiments demonstrate the superiority of the ANN-DPC over existing counterparts.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2024.111748