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07.09.2022

ROCM: A Rolling Iteration Clustering Model Via Extracting Data Features

verfasst von: Linliang Guo, Limin Wang, Xuming Han, Lin Yue, Yihang Zhang, Minghan Gao

Erschienen in: Neural Processing Letters

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Abstract

The allocation of boundary points and low-density clusters has become an essential part of clustering research. Most of the recent improved methods that focused on identifing allocation of points have not addressed the issue of specific data point assignment in terms of the data’s distribution feature. In this article, a rolling iteration clustering model (ROCM) was proposed for assigning the specific data point by extracting the feature of data points. In this model, data points were transformed into multiple units with different distribution structures, and then each unit’s dispersion used to discover representative groups was analyzed. Sparse data were clustered based on the proposed self-expansion principle to effectively capture boundary points and assign points at joint. Furthermore, the rolling iteration module avoided the over-partitioning and chaining effect and discovered clusters with diverse shapes and densities. Experimental results of twenty-two datasets proved the effectiveness of the proposed method. ROCM has better performance than other state-of-the-art methods.
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Metadaten
Titel
ROCM: A Rolling Iteration Clustering Model Via Extracting Data Features
verfasst von
Linliang Guo
Limin Wang
Xuming Han
Lin Yue
Yihang Zhang
Minghan Gao
Publikationsdatum
07.09.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-10972-w